Beyond Haraway? Addressing constructive criticisms to the ‘four epistemic gaps’ interpretation of positionality and situated knowledges
Bibliographic record
Abstract
The ‘four epistemic gaps’ interpretation of positionality and situated knowledges developed in the anchor article goes significantly beyond Donna Haraway’s original formulation of the thesis of situated knowledges. It does so by organizing the study of the processes that provincialize and politicize perception and cognition alongside a logical sequence of epistemic gaps that shape the quantity and content of information accessible to different subjectivities. In this contribution, I address four sets of productive tensions and constructive criticisms sparked by the anchor article and highlight how they can help fulfill the promise of a generative research program that engages multiple other voices.
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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 | 0.068 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.108 |
| Scholarly communication | 0.020 | 0.039 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".