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Record W7094300676

Comment survivre aux controverses sur le transport à Québec?

2022· other· fr· W7094300676 on OpenAlexaboutno aff

Bibliographic record

VenueCorpus Université Laval (Université Laval) · 2022
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPortraitOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

La grande région métropolitaine de Québec est, à certains égards, un concentré d'histoire de l'urbanisme nord-américain. Construite stratégiquement sur un promontoire rocheux, ses rues sinueuses et son développement ont d'abord été pensés à échelle humaine: il fallait marcher les rues. Plus tard, le cheval, et ensuite le tramway électrique ont amélioré le confort du transport, sans toutefois bouleverser les habitudes. Avec les années 1960 arrivent la modernité, sa folie des grandeurs et, surtout, l'essor de la voiture individuelle. Subitement, les banlieues deviennent accessibles. Des quartiers sont rayés de la carte, les grands boulevards balafrent le paysage et la ville s'étale. Plus d'un demi-siècle plus tard, voilà qu'on se remet à rêver de tramway, de réseau structurant… et d'un pharaonique projet de tunnel reliant les centres-villes de Québec et de Lévis. Comment comprendre toutes ces options? Sont-elles contradictoires? Quelles sont les forces en présence qui tentent d'influencer le débat citoyen? En conjugant leurs savoirs, Jean Dubé, Jean Mercier et Emiliano Scanu dressent le portrait de la situation et nous offrent certaines clés de lecture pour comprendre ce débat qui rythme la vie sociale, économique et politique de Québec depuis de nombreuses années. Et pour tester leur analyse, deux personnalités au regard critique, Yvon Charest et Jérôme Landry, donnent leur avis sur cet essai éclairant.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.071
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0210.010
Scholarly communication0.0100.007
Open science0.0030.002
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0260.002

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.

Opus teacher head0.011
GPT teacher head0.182
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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