Bibliographic record
Abstract
A personal advertisement constitutes a distinct form related to the small ad family of genres. While small adstraditionally offer an object or a service, the personal ad offers but, most essentially, seeks a romantic partner. Todate, studies of personal ads have mainly focused on patterns of represented traits in relation to identity, gender,age and sexuality in the verbal text. Given the self-promotional nature of the genre, image is also a powerful toolused as one of the resources for representing identity and engaging with others. Using social semiotic perspectiveand the framework of systemic functional linguistics, this study focuses on how identity is verbally and visuallyrealised in online personal ads. This paper has two aims: the first is to show how resources from verbal andvisual systems combine and complement one another to construe a variety of personal and social traits,clustering into different identity types. The second is to indicate the usefulness of these descriptions infacilitating a multimodal approach to the analysis of identity. The results revealed a convergence of verbal andvisual resources in identity performances, construing the slim and attractive woman and the funny but sensitiveguy, both aimed at invoking interest from potential partners. Identities emerged through the use of nominalgroups and processes and the categorizations associated with these resources. Images that are displayed on theprofile pages contain features that correspond to the tendered traits in the verbal description creating a holisticperformance of online identities.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".