Playful embodiment: Body and identity performance on the Internet
Bibliographic record
Abstract
Aquest article analitza algunes de les pràctiques relacionades amb la presentació del cos a Internet. Concretament, ens centrem en la relació entre el cos i l'actuació de la identitat en les interaccions online, comparant els jocs d'identitat en les primeres etapes d'Internet basat en el text i la revelació de la identitat en les xarxes socials tot utilitzant les actuals tecnologies multimodals. Proposem que mentre el joc amb l'anonimat caracteritzava l'Internet textual, actualment trobem una "encarnació lúdica" del cos a Internet en el procés de producció, difusió i consum d'imatges del propi cos que la gent fa a través de la xarxa. El treball empíric es basa en l'anàlisi de blogs i fotologs espanyols i llatinoamericans que utilitzen el cos com el seu eix principal. Abstract This paper discusses practices related to the presentation of the body on the Internet. We focus on the relation between body and identity performance in online interactions, comparing identity play in the early stages of text-based Internet and in current multimodal networking technologies. We argue that, while earlier practices were characterized by playing on anonymity, people are now engaged in a "playful embodiment" process in relation to the production, diffusion and consumption of people’s images of their own bodies through the Web. The empirical work is based on the analysis of Spanish and Latin American (photo) blogs that focus mainly on the body.
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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 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".