Panizzi, Lubetzky and Google: How the Modern Web Environment is Reinventing the Theory of Cataloguing
Bibliographic record
Abstract
This paper uses cataloguing theory to interpret the partial results of an exploratory study of university students using Web search engines and Web-based OPACs. The participants expressed frustration with the OPAC; while they sensed that it was “organized,” they were unable to exploit that organization, and attributed their failure to the inadequacy of their own skills. In the Google searches, on the other hand, students were getting support traditionally advocated in catalogue design. Google gave them starting points: resources that broadly addressed their requirements, enabling them to get a greater sense of the knowledge structure that would help them to increase their precision in subsequent searches.Cette étude utilise la théorie du catalogage pour interpréter les résultats partiels d'une recherche exploratoire d'étudiants universitaires utilisant les moteurs de recherche Web et les catalogues publics en ligne. Les participants ont exprimé leur frustration envers les catalogues publics en ligne. Bien qu'ils percevaient que les catalogues sont "organisés", ils ont été incapables d’utiliser cette organisation et ont attribué leur échec au manque d'adaptation de leurs propres capacités. Lors de recherches avec Google, d'autre part, les étudiants ont reçu l’assistance traditionnellement proposée dans la conception d’un catalogue. Google leur a donné des points de départ : ressources qui répondent largement à leurs besoins, leur permettant ainsi d’obtenir une meilleure compréhension de la structure des connaissances qui pourraient les aider par la suite à augmenter leur précision lors de recherche.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.019 | 0.044 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".