Reason giving, city icons and the culture of cities: data from a radical interpretive perspective
Bibliographic record
Abstract
From a radical interpretive perspective, the distinction between primary and secondary data is itself secondary. Taking as a case study sample newspaper accounts for the motive behind the famous hockey venue move in Montreal (from the Forum to the Molson Centre), this paper will demonstrate what is involved in treating this talk as a discourse. By testing these newspaper accounts, the paper will show that a sole focus on the empirical accuracy of such motive characterizations can fail to take up the way the accounts, when understood as a discourse, are themselves ways of accessing how social phenomena emerge as objects in the world. The latter analysis, it will be claimed, makes for a more grounded understanding. Specifically drawing on Blum and McHugh’s (1971) analysis of motive talk, the paper will demonstrate the insights into the culture of cities that a radical interpretive perspective can make in this case.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".