Bibliographic record
Abstract
El dia 8 de marzo del 2012 el Dr. Ian Hacking concedio esta breve entrevista a Protrepsis en el marco de su visita al Instituto de Investigaciones Filosoficas de la Universidad Nacional Autonoma de Mexico. El Filosofo canadiense viajo a Mexico para impartir la Catedra Gaos, bajo el nombre de: The Mathematical Animal. Philosophical thoughts about mathematics as an human activity.Ian Hacking es uno de los filosofos de tradicion analitica mas importantes en la actualidad. Se le considera uno de los exponentes mas representativos del realismo cientifico, junto con H. Putman y Niniluoto, entre otros. Actualmente es profesor de filosofia en la Universidad de Toronto y del College de France.Es autor de libros de tematicas muy variadas, que van desde la probabilidad y estadistica hasta el aspecto social de la ciencia, pasando por la logica. Algunas de estas obras han sido traducidas al espanol, como: ?La construccion social de que?, Representar e intervenir y La domesticacion del azar, entre otros.La platica giro en torno a L. Wittgenstein, por tratarse del tema abordado en el Dossier de este numero de Protrepsis.La entrevista se llevo a cabo en ingles, a continuacion se presenta la traduccion de la misma al espanol y posteriormente la transcripcion en el idioma original.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".