Bibliographic record
Abstract
Knowing Otherwise: Race, Gender, and Implicit Understanding (2011) is an important, ambitious book that I admire greatly and whose aims I support.I marvel at the reach and complexity of her project and the grace with which she has integrated its various threads.I wholeheartedly agree that the various forms of nonpropositional knowing that Shotwell articulates are extremely important.As she acknowledges, there have been a few philosophers who have written on these forms of knowledge/understanding, but we would all be a lot better off in our epistemology, political philosophy, and political action if many more of us try to encompass this kind of work.Nevertheless, I am a bit overwhelmed by the sheer number and kinds of changes we would need to make to begin explicitly incorporating nonpropositional knowledge into our projects.I first need to get my bearings by doing some sorting, then I will turn more concretely to guilt and shame. Getting my bearings.Much of what interests Shotwell is knowledge that cannot ever be captured fully in propositions, namely, her categories 1: skill knowledge, 2: the intersection of somatic and conceptual understanding, and 4: emotional knowledge.1 I would add another category to this: knowledge by acquaintance-of other people, not of "sense-data."Shotwell
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.046 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".