Bibliographic record
Abstract
Regresion es la sexta pelicula de Alejandro Amenabar, el que fuera nino prodigio del cine espanol con sus dos famosos thriller de los anos 90: Tesis (1996) y Abre los Ojos (1997), o el filme de terror Los Otros (2001), su debut en el cine de habla inglesa. Y con el presente titulo, precisamente, retorna a ese mismo genero que le dio la fama, con una historia protagonizada por Ethan Hawke y Emma Watson. Ambientada en un pequeno pueblo del interior de EEUU a finales de la decada de los 80, Regresion se centra en la investigacion de una serie de crimenes satanicos que es llevada a cabo por un detective (Hawke) con la ayuda de un psiquiatra (David Thewlis) y una peculiar tecnica de hipnosis que hace que la mente de los testigos vuelva al lugar de los hechos. Pero la investigacion, lejos de aclarar lo sucedido, comienza ademas a complicar la vida del detective, cada vez mas obsesionado con los extranos acontecimientos que rodean al caso. Rodada en Canada y con financiacion americana, Regresion vuelve a explorar los recovecos de la mente humana, de los suenos y de los recuerdos, de forma similar a la ya citada Abre los Ojos. Ethan Hawke carga con el peso de una pelicula, la tercera de Amenabar rodada en ingles, llena de sorpresas y en la que cada personaje y cada situacion parecen esconder algo.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".