A Digital Archaeology of Early Hispanic Film Culture: Film Magazines and the Male Fan Reader
Bibliographic record
Abstract
As a sociologically-oriented study, this project contributes to an archaeology of cinema fandom broadly, and early Spanish fan culture specifically, by spotlighting male readers of popular film magazines. Taking as an exploratory case study reader interactivity with the magazine _Popular Film_, analysis of correspondence and published photos of readers participating in reader contests demonstrates that the magazine’s cinema fan base was composed of a strikingly large proportion of readers who were male and that these were ardent enthusiasts of celebrity consumer culture. This is a notable contradistinction to the widely-held idea of the star-struck female movie fan. Methodologically, in conducting this study we reflect on the challenges of digital approaches to historical periodical research, where particular challenges are posed when working with magazines in a non-anglophone language, and when there are few baseline studies to rely on to guide and contextualize patterns picked up through strictly macro methods. We advocate for the affordances of a mixed macro-micro approach that combines distant reading with traditional textual studies of close reading. By adopting such a hybrid framework, digital methods provide new opportunities towards reconstructing profiles of magazine readerships and to unearth evidence of male movie fans in Spain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".