Toward the Interested Investigator: Examining the Epistemic Dimensions of Relational Theory in Zoology
Bibliographic record
Abstract
I argue in this thesis that a research approach that is based on a meaningful relationship between a zoologist and their study subject holds unique epistemic value. It has been well established by feminist and social epistemologists that the identity and interests of the knower significantly influence the way knowledge can be created. This has been applied in philosophy of science to recognize that the identity of the scientist significantly influences the way they create scientific knowledge. Relational theorists draw our attention to the uniqueness of relationships themselves. I seek in this thesis to draw our attention to the potential epistemic influence of relationships themselves in the creation of scientific knowledge in biology. I do this by bringing together relational theory and epistemologies of ignorance to understand how scientists might influence knowledge creation in biology. I do this with a specific focus on zoology. \nIn Chapter 1, I perform a literature review of relational theory and its applications in the philosophy of biology. I argue here that relationality presents a fruitful axis for analyzing research and knowledge creation in zoology. Next, in Chapter 2, I present what I call a researcher’s “state of interest toward relationality”. I argue here that relational theory can be used to differentiate research approaches used by zoologists. I do this using two case studies: that of Jane Goodall’s research program with chimpanzees and Eugenie Clark’s research program with sharks. Finally, in Chapter 3, I use relational theory to analyze the creation of ignorance in zoology. I argue here that a research approach where a zoologist has a high state of interest toward relationality offers unique epistemic benefits by allowing zoologists to circumvent and/or respond to the creation of some forms of ignorance. I hope that by connecting relational theory with the epistemology of ignorance in biology, we may see ways that relationships can strengthen scientific research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.074 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".