Zihinsel Haritaların Biçimlenmesinde Ulaşım Ağı Bağlamında Yerleşme Tipolojisinin Etkisi; Toronto Örneği
Bibliographic record
Abstract
Zihinsel harita, kullanicilarin zihninde olusan, temelde yol ve yon bulmayi saglayan, zihinsel model olarak da tarif edilen olgudur. Zihinsel harita kisaca kullanicinin bir yerle ilgili zihnindeki imgelerin tumunu anlatmaktadir. Yapilan arastirmalar zihinsel haritalarin kullanicinin ozelliklerine gore bicimlenmesinin yani sira cevrenin ozelliklerine gore de bicimlendigini gostermektedir. Bu calisma ile topografya ve buna bagli olarak ulasim agi gibi cevresel ozelliklerin zihinsel harita tiplerini nasil etkiledigi ele almaktadir. Appleyard kullanicilarin cevreyi ulasim sistemi baglaminda algiladigini, bu nedenle zihinsel haritalarin cevrenin ulasim sistemine gore bicimlendigini savunmustur. Yaptigi bir arastirmada bu savdan yola cikarak zihinsel harita tipolojisini tarif etmistir. Zihinsel haritalar ile ilgili yapilan diger arastirmalar da bu bulguyu desteklemektedir. Ancak ulasim aginin topografya ve yerlesme kulturune bagli olarak organik bicimlendigi dokularda, zihinsel harita tipinin degistigi saptanmistir. Bu yerlesmelerin haritalarinin zor kavranan ulasim agi yerine, daha kolay akilda kalan isaret ogelerine gore bicimlendigi gorulmustur. Bu calismada zihinsel haritalar ile ilgili arastirmalardaki saptamalar, bir kez daha duz bir alanda, izgara sistemde kurulmus bir yerlesmede sinanmistir. Arastirma icin duz bir topografyada, izgara bicimli ulasim sistemine sahip olan Toronto kenti secilmistir. Calismanin yontemi, gozlem calismalarina ek olarak, Toronto Universitesi’nde gerceklestirilen yazili ve cizili ifadeleri iceren anket calismasini kapsamaktadir. Bu ankette katilimcilardan bazi bilgilerin yani sira kentin haritasinin cizilmesi istenmistir. Elde edilen veriler ulasim sisteminin, zihinsel harita tipini etkiledigini gostermektedir.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".