Bibliographic record
Abstract
RIBASLa metropolis contemporania orienta les seves transformacions segons models organics i funcionals: un centre historie on la ciutat es muse'ltza, arees residencia ls on viu la gent, centres comercia ls on anar a comprar, zones in dustrials on es fabri quen les mercaderies, intercanviadors de transit, etc. Avui día l'experi emcia de la gran ciutat es pot expli car en termes de moviments entre zones especialment dissenyades pera activitats perfectament planejades.Malgrat l'esforv deis po lítics i els urbanistes per entreteixi r el comp lex ordit del pa isatge urba, entrem ig de la trama d'interessos i objectius que constitueix cada ciutat hi ha racons que fugen del seu control.Són els terrains vagues: descampats, zones intersticials que no han estat ocupades per la indústria ni per la via ferria; espais residua ls a les ribes deis rius, abocadors, pedreres, franges de terra sense utilitzar dibuixades per les carreteres, que esperen els especuladors i promotors ... Així és el panorama que trobem en una gran ciutat com Barcelona -tot i la ciru rgia estetica a la qual va ser sotmesa per urbanistes i arquitectes pera les Olimpíades del 1992. 1 aquest és el territori on Xavier Ribas ha decidit treballar.Xavier Ribas disecciona el fenomen de l'entreten iment, de l'oci, del que la gent fa en el seu «temps lliure», tot mostrant els espais residuals de la ciutat on tenen ll oc aquest es activitats.Molt espontaniament la gent preserva
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.116 | 0.041 |
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".