The Animals Reader: The Essential Classic and Contemporary Writings (Linda Kalof y Amy Fitzgerald. Eds.)
Bibliographic record
Abstract
Las propuestas explicativas de la sociobiología y, aún más recientemente, de la psicología evolucionista han suscitado la queja, entre los científicos humanos, de una "colonización de las ciencias sociales" (término de Rose, 2000) desde la biología. La queja es legítima, si no por otros motivos, por una lamentable tendencia a la simplificación de las intricadas redes conductuales humanas a través de "explicaciones evolutivas". Pero no podemos olvidar que hay un movimiento de igual tamaño e impacto desde las ciencias sociales que, a ejemplo de Bruno Latour, Tim Ingold y tantos otros, "colonizan" los estudios y objetos naturales (ver, por ejemplo, Latour, 1996 e Ingold, 2008). En The Animals Reader, dos sociólogas - Linda Kalof, de la Universidad del Estado de Michigan, y Amy Fitzgerald, de la Universidade de Windsor, Canada - son las responsables de hacer una incursión en un territorio tradicionalmente natural y, por tanto, perteneciente a las ciencias de la naturaleza: las descripciones que hacemos y las relaciones que mantenemos con los animales. The Animals Reader es una compilación bien cuidada, pienso que la primera de su tipo, que da forma a una preocupación multidisciplinar: los "estudios animales".
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".