Eina dinàmica per analitzar dades de mobilitat de la comunitat UPC recollides amb MobilitApp
Bibliographic record
Abstract
Aquest informe és un breu resum preliminar d’un article que properament es publicarà a la revista “Ítem: revista de biblioteconomia i documentació” del Col·legi Oficial de Bibliotecaris i Documentalistes. Del 15 al 19 d’abril de 2024 es va celebrar a la UPC la campanya “I tu com vens al campus?” organitzada pel grup de recerca Smart Services for Information Systems and Communication Networks (SISCOM) de la Universitat Politècnica de Catalunya - BarcelonaTech (UPC) i pel Gabinet d'Innovació i Comunitat (GIC) de la UPC, amb la col·laboració de l’Autoritat del Transport Metropolità (ATM) de Barcelona. Aquesta prova pilot s’ha implementat durant una setmana al Campus Diagonal Nord, a Barcelona. Ha permès recollir dades de qualitat en relació amb la mobilitat de l’estudiantat, el PTGAS i el PDI, concretament dels seus trajectes multimodals. L'objectiu final del projecte és millorar la mobilitat de la comunitat UPC a l'hora d'accedir als campus, a partir de l’anàlisi de les dades recollides col·lectivament, sempre respectant la privadesa de dades. El projecte s’inscriu dins de les iniciatives de ciència oberta i ciutadana de la UPC.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".