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Record W7110501723

ENDÜSTRİ ÖZNELERİNİN ÇALIŞMA, YAŞAM VE KOLLEKTİVİTE MEKANLARINDA OLUŞUMU: ESKİŞEHİR 1923-1980

2021· other· W7110501723 on OpenAlexaboutno aff

Bibliographic record

VenueOpenMETU (Middle East Technical University) · 2021
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFactory (object-oriented programming)TurkishQuarter (Canadian coin)PoliticsState (computer science)Industrial cityIndustrial relationsUrban environment
DOInot available

Abstract

fetched live from OpenAlex

State-based industry in Eskişehir was introduced by state-owned factories in the second quarter of the twentieth century. Accordingly, three large-scale industries, the railway factory, the sugar factory, and the Sümerbank print factory, were founded or developed by the Turkish state. In line with these developments, the city began to be transformed through industrialization, migration, and urbanization. Between 1950 and 1980, the industrial workers in Eskişehir began to find their own voices and took part in intense organizational debates within the workers’ organizations: trade unions, editorial rooms, consumer cooperatives, and holiday camps. Thus, many industrial workers spread to the larger urban environment, searching for living and collective spaces, struggling to form organizations as organized industrial subjects, and interacting with the social and cultural life of the city. In addition to what the state introduced, this dissertation discusses how the industrial employees produced in and interacted with the urban environment by covering all employees working in the factory - managers, officials, engineers, workers and other employees - to explore a more diverse network of actors. The main objective is to understand how work, living, and collective spaces were produced and used resulting in multiple industrial subjects alongside an analysis of how this built environment was positioned within social, economic, and political change in the city.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0070.009
Meta-epidemiology (broad)0.0090.005
Bibliometrics0.0090.020
Science and technology studies0.0030.005
Scholarly communication0.0020.002
Open science0.0150.016
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.1070.043

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 head0.040
GPT teacher head0.224
Teacher spread0.184 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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