Bibliographic record
Abstract
Imagineu que un amic us relata què va fer durant el cap de setmana, o que sou vosaltres qui ho expliqueu a alguna persona o que ho rememoreu per a vosaltres mateixos. Podríem fer-ho descrivint sobretot els detalls sensorials que vam experimentar, com per exemple el vent fresc, els colors del capvespre o l’aroma del cafè; o centrar-nos més aviat en els pensaments i emocions que ens va evocar l’experiència, com ara que ens vam sentir lliures, que vam rememorar somnis antics que teníem mig oblidats o que vam notar que alguna cosa havia canviat dins nostre. Tot i que les dues històries estarien narrant uns mateixos fets objectius, la manera com les presentem afecta completament com les recordarem després. Un estudi recent liderat per la psicòloga Signy Sheldon de la universitat McGill al Quebec (Canadà), suggereix que aquesta diferència no és anecdòtica. La manera com expliquem o escoltem històries determina com el cervell les emmagatzema. Segons aquest treball, que ha estat publicat al Journal of Neuroscience, narrar una història viscuda posant l’èmfasi en els detalls sensorials o en els emocionals activa xarxes cerebrals diferents, i aquestes diferències influeixen en la retenció de la informació essencial de la història.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".