Bibliographic record
Abstract
Kith and KinKith is an odd word, rolling off the tongue awkwardly.It only ever seems to show up attached to kin, but has a long lineage in English of at least a thousand years.At one point it meant 'knowledge of something', or the 'land you are from', but now has come to mean something close to 'friends' or everyone you are close to who is not a direct relative.It gestures toward a phrase we are interested in: good relationships, which might be a synonym for substantive concern for the other.Kin is a word in much more common circulation, recognizable on the covers of hipster lifestyle magazines and TV shows revolving around the 'unbreakable bonds of blood and family' that are repeatedly broken.Kin evokes family primarily, but gestures to larger values of hearth-andhome, of simpler fidelities and wholesomeness.Certain environmental movements enthusiastically call for us to view the more-than-human as kin in the hope that if we see other forms of life as family, we will recognize them as being just like us, and thus we will be less likely to damage them.Donna Haraway has famously written often about 'making kin' with other animals, notably in When Species Meet and Staying with the Trouble: Making kin seems to me the thing that we most need to be doing in a world that rips us apart from each other [...] By kin I mean those who have an enduring mutual, obligatory, non-optional, you-can't-just-
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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".