Bibliographic record
Abstract
A personal advertisement has two aims; to promote its author and to attract potential love interest. In addition to providing textual information about physical appearance, occupation and interests, accompanying images in personal ads create meanings that are instrumental in building imaginary relations between the advertiser and the readers. This paper explores the notions of body language and interpersonal attitude and courtship initiation behaviour in online personal ads. Using Kress & van Leeuwen’s Grammar of Visual Design (2006) and Mehrabian’s dominant and affiliative dimensions (1981) this study examines how interpersonal relations are represented through the embodiment of affiliative and non-affiliative attitudes in images. Analysis of 581 images reveals several common visual personas through specific clusters of bodily stance and facial articulation that are instrumental in creating and establishing represented affiliation between the participants in the images and the viewers. As such this study is a contribution to scholars working in the area of visual analysis, identity and social semiotics as it identifies non-verbal realizations of affiliative and non-affiliative attitudes and demonstrates their interaction through a corpus-based analysis of personal ad images.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".