Lecture Notes ways of knowing - world food, pop and developemt
Tacit knowledge, knowledge types, and the epistemic foundations of development
The lecture centers on knowledge, with explicit reference to tacit knowledge (tacit knowledge is briefly described as tacit experience or tacit know-how). The instructor highlights tacit knowledge as a key term to explore in class.
Practical classroom logistics around attendance:
If you’re sick or have a doctor’s appointment, you can let a TA know or not; it’s not mandatory to notify the instructor for every absence.
If attendance is taken in Brightspace and you see a zero due to illness, you can follow up after the fact explaining what happened and you’ll likely receive credit.
The instructor is not upset if you don’t report every absence; transparency is optional.
Weekly analysis assignment: structure and logistics
Due Sunday at midnight.
You can base it on today’s lecture, Thursday’s lecture, or a blend of both.
Prompt: take something you find interesting from the lecturer or reading and connect it to another idea or reading.
Method: describe why you find it interesting, what it connects to, and include a Google-news or news story if relevant.
Length: 250–400 words (2–4 short paragraphs).
Grading rubric:
2 points for quality of thinking
2 points for connection to course material
1 point for grammar, spelling, citation structure, etc.
The assignment emphasizes synthesis, critical thinking, and engagement with course readings.
Flexibility in sources for the weekly analysis:
You can reference today’s lecture, Thursday’s lecture, or both in combination if relevant.
The goal is to foster conversation between class content and readings, including cross-referencing ideas.
Class discussion: reflecting on weekends as a lens for measuring well-being
The instructor invites students to reflect on what makes a weekend “good” and how to judge it.
Qualitative factors discussed (examples from the dialogue):
The number and type of activities you enjoyed.
Whether you spent time with people who fulfilled you (social fulfillment vs. sheer quantity of company).
Rest and refreshment: whether you woke up rested and felt prepared for the week.
Laughter and mood: whether you laughed a lot; humor is a relatively observable indicator of well-being, though still subjective.
Personal preferences: some prefer being around people, others prefer quiet activities like Netflix, games, or reading.
The lecture uses a spectrum of responses to illustrate that “a good weekend” can be understood along multiple axes (social fulfillment, rest, enjoyment, mood).
A hypothetical: how to quantify weekend quality for policy or research purposes
Proposed method: a survey on a scale of 1–5 asking about overall happiness, rest, laughter, and other dimensions (e.g., restfulness, laughter).
Example outcome: average scores by day of week (e.g., Mondays–Sundays) to identify patterns in happiness.
Acknowledges biases: some students may overstate or understate happiness due to survey fatigue or social desirability.
Debates about the value of numerical measurement vs qualitative understanding: some students prefer simple yes/no questions (happiness yes/no) or less numerical approaches.
Philosophical point: even when numbers are used, there is an epistemic choice about what constitutes a valid metric and what kind of knowledge is generated (quantitative vs qualitative).
Real-world implication: metrics of well-being are policy-relevant but normative—what counts as “good” depends on context, values, and measurement tools.
Development discourse: explicit knowledge, tacit knowledge, and types of knowledge
Core idea: we generate knowledge in multiple ways, including explicit knowledge (codified, easily communicated) and tacit knowledge (know-how that is often difficult to articulate).
Other knowledge types mentioned: scientific knowledge and indigenous knowledge, highlighting diverse ways of knowing and producing knowledge.
The course aims to acknowledge implicit biases students bring from their backgrounds (e.g., geographic or cultural contexts) and to encourage thinking beyond those biases, especially in sustainability work.
Practical emphasis: connect knowledge generation to real-world relevance in development and sustainability.
Foundational philosophy: metaphysics, ontology, and epistemology
Metaphysics (high-level questions about reality):
Central questions: What is the nature of reality? Are we living in a matrix, or is this a conventional perception of reality?
The discussion frames how beliefs about reality shape subsequent questions and research directions.
Ontology (the study of what exists and the relationships between things):
After establishing what is real, we examine how entities relate within that reality.
Example provided (Seven Years in Tibet): digging canals involved ethical constraints about killing animals, illustrating an ontological stance about the relationship between humans and other beings.
Ontology can influence views on rights, humans’ place in the cosmos, and social classifications (e.g., class and social structure).
Epistemology (the theory of knowledge):
Once reality and relationships are settled, epistemology asks: what is knowledge, and how do we know what we know?
Questions include: Can our senses be trusted? How do we know that knowledge is trustworthy? How do we acquire knowledge?
The lecture links epistemology to practical questions about knowledge claims (e.g., a claim about weekend happiness or climate change) and the role of context in judging knowledge validity.
Context and bias in epistemology:
The instructor notes that context matters: personal circumstances (e.g., being single, married with kids, mortgage, or income) alter how we interpret information and metrics.
In development studies, context (e.g., smallholder farmers in Sub-Saharan Africa) shapes what success and well-being mean and what metrics are appropriate.
The overarching aim: recognize diverse epistemologies and biases, while striving to expand analytic horizons in sustainability work.
Science and the scientific method: definitions, processes, and epistemic standards
What is science?
Science is a method of thinking about the world using the scientific method: observation, questioning, hypothesis, testable predictions, data collection, analysis, refinement, replication, and generalization.
Empirical knowledge can be qualitative or quantitative.
Empirical evidence is typically treated as objective, testable, measurable, replicable, and generalizable.
The scientific method in practice (tree-water example):
Observe that trees respond to water status (green vs brown) and formulate a hypothesis about water needs.
Develop testable predictions (e.g., trees with different water amounts will show different browning patterns).
Collect data across plots with varied water treatments and analyze results.
Refine hypotheses (e.g., too much water can also cause browning).
Integrate findings with existing scientific literature and run additional experiments as needed.
Key concepts in empirical science:
Objective: knowledge claims are evaluated independently of the observer.
Testable: hypotheses must be falsifiable or testable through experiments.
Measurable: quantities can be quantified or measured.
Replicable: others should be able to reproduce the results using the same methods.
Generalizable: findings apply beyond the specific study context to broader cases.
Terminology examples:
Vaccination efficacy example: efficacy is the percentage of the population not developing severe complications from a disease; this can be expressed as a ratio or percentage and calculated across populations.
Gravity example: the rate of fall in a vacuum is roughly
with acceleration in standard Earth gravity.
The peer review process (how science is validated and published):
Start by submitting a manuscript to a journal; editors decide whether to send it out for peer review.
Peer reviewers (usually 2–3) provide feedback; the author revises accordingly.
The editor may request changes or reject; if accepted, the paper becomes part of the scientific literature.
The author shares experiences of journal matching (e.g., wrong journal rejection, then resubmission to a better fit).
A real-world example from the instructor’s experience: a paper on monetary policy and the World Bank data being evaluated through Cambridge Journal of Economics after initial rejection elsewhere.
The peer review process can involve iterative back-and-forth revisions over weeks to months.
Epistemology in science and debates about methods:
The current discussion mentions a paper in review that argues statistics import an ontological perspective (numbers as a primary way of knowing) and that there may be alternative epistemologies beyond statistics.
The reviewers may challenge such a claim, requesting additional justification and evidence.
How science shapes modern societies:
Scientific claims about climate change are presented as well-supported through observation, testing, replication, and peer review.
The credibility of scientific claims rests on methodological rigor and consensus built through scientific processes.
Qualitative vs quantitative approaches and context in measurement
The course emphasizes multiple ways of knowing and measuring well-being, development outcomes, and other phenomena.
Discussion points include:
The value and limits of numerical surveys (e.g., 1–5 scales) for capturing complex experiences like happiness.
The role of qualitative methods (interviews, focus groups, narratives) as complementary or alternative approaches.
The importance of context in interpreting metrics (what counts as a good weekend or a successful development outcome depends on situational factors).
Practical implications for development research:
Metrics should be chosen with explicit attention to what is being measured and why.
Implicit bias and context should be acknowledged when building metrics for policy or research.
A combination of quantitative and qualitative methods often yields more robust insights.
Miscellaneous classroom anecdotes and affiliate tasks
A humorous aside about a shelving storefront or bookstore experience: the speaker encountered issues ordering a course book online, a glitch that prevented viewing or purchasing the item.
A practical interaction about obtaining a course packet for a test: the recommended steps include visiting the upstairs customer service checkout counter, filling out information, and paying; the packet would be ready in about two business days.
An aside about a non-sequitur classroom event: a discussion about joining a Labor Day protest in downtown, with a brief reflection on the experience and the speakers.
A short break in the dialogue with a quick check-in: a student asks about how to proceed, and the instructor offers a quick exit and says goodbye.
A note on pacing and time: a five-minute window is mentioned at the end of the transcript.
Key terms and ideas to remember for the exam
Tacit knowledge: knowledge that is difficult to transfer to another person by writing it down or verbalizing; often learned through experience.
Explicit knowledge: codified, articulable knowledge that can be transmitted in words and numbers.
Metaphysics: study of the nature of reality and existence.
Ontology: study of the nature of being and the relationships between entities within reality.
Epistemology: study of knowledge—what it is, how we know it, and the justification of beliefs.
Empirical science: knowledge gained through observation and experiment; can be qualitative or quantitative.
Objectivity vs subjectivity: science aims for objectivity, but context and interpretation introduce subjective elements.
Replicability and generalizability: core standards for scientific claims.
Peer review: validation process for scientific work through evaluation by experts in the field.
Development and sustainability metrics: metrics can be numerical or qualitative and must be interpreted within their context and epistemological framework.
Ethical and practical implications: how knowledge, measurement, and policy interact with real-world decisions and values.
Metrics and numerical examples referenced in the lecture
Weathering the ambiguity of weekends on a scale:
A hypothetical survey could use a scale from 1 to 5 to rate weekend happiness, restfulness, and laughter.
Example result interpretation might reveal which days show higher happiness on average.
Gravity constant example:
The rubric for the analysis assignment:
points total (2 points for quality of thinking, 2 for connection to course material, 1 for grammar and citations).
Connections to broader themes and real-world relevance
The epistemological questions raised (what is knowledge, how do we know, how to measure) are central to sustainable development practice, where metrics often drive policy decisions.
The discussion about context, bias, and different knowledge systems emphasizes inclusive, multi-perspective approaches to development challenges.
The science segment reinforces how robust knowledge is produced and communicated, highlighting the importance of methodological rigor, transparency, and peer review for public trust.
Ethical implications: decisions about what to measure and how to measure influence policy, resource allocation, and the lived experiences of communities.
Quick glossary (based on the lecture content)
Tacit knowledge: knowledge that is difficult to articulate or transfer verbally; gained through practice and experience.
Explicit knowledge: codified knowledge that can be easily communicated in words or numbers.
Metaphysics: study of the fundamental nature of reality.
Ontology: study of the kinds of beings and their relationships in reality.
Epistemology: study of knowledge—its sources, structure, and validity.
Empirical: knowledge gained through observation or experience; can be qualitative or quantitative.
Replicable: able to be reproduced by others following the same methods.
Generalizable: findings extend beyond the studied sample to broader contexts.
Peer review: evaluation of scholarly work by experts to ensure quality and credibility.
Sustainable development: development that meets the needs of the present without compromising the ability of future generations to meet their own needs; requires careful consideration of context and metrics.
Note: The content above consolidates the key ideas and examples from the transcript into comprehensive study notes. Use these notes to prepare for exams and to guide your understanding of how knowledge, science, and development interact in real-world contexts.