Yerel Kimliğin Mekânsal Temsili ve Québec Kentinde Korunması
Bibliographic record
Abstract
Kentte yasayanlarin sosyal, kulturel ve ekonomik yasama bagli olarak kentsel mekânda tanimladigi degerler, semboller ve islevler kentin kulturel kimligine dair referanslar tasimaktadir. Bu baglamda, kentlinin yasadigi cevreye yonelik edindigi duygular, inanclar ve anilar ile birlikte cevreyi tanimlama bicimi o kentin kulturel kimliginin tamamlayici parcasini olusturmaktadir. Bu nedenle tarihi kentsel cevrelerin kimliginin belirlenmesinde sadece fiziksel nitelikler yeterli olmamaktadir. Fiziksel niteliklerle birlikte kullanicilar tarafindan kentsel mekâna yuklenen anlam ve kullanim bicimlerinin de degerlendirilmesi gerekmektedir. Bu makale, tarihi cevrelerin korunmasinda sosyal, kulturel ve fiziksel niteliklerin birlikteligi ile olusan yerel kulturel kimligin korunmasinin onemini aciklamayi amaclamaktadir. Bu kapsamda, cagdas koruma yaklasimlari cercevesinde tarihi kentsel cevrenin korunmasinda yerel kimligin onemi vurgulanmakta, kentsel olcekte yerin kimliginin korunmasindaki temel sorunlar ve cozum onerileri tespit etme, koruma ve aktarma basliklari altinda irdelenmektedir. Irdelenen konular, cagdas koruma yaklasimlarinin yansitildigi Quebec tarihi kent merkezinde yazarlarin yapmis oldugu alan tespit calismasiyla orneklenmektedir.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".