Super Tagging in the Development of Sexual Nomenclature and Social Organization Online. Advances In Classification Research Online
Bibliographic record
Abstract
In this paper, I describe the ways in which interventionist forms of tagging, such as super tagging, guerilla tagging, and tag bombing within Xtube, a database of sexual representation, reveal complex social and cultural structures among members of sexual subcultures and point to the particularlity of various modes of sexual being and the relationship between those modes and particular configurations of sexual identity. Individuals who participate in super tagging do not necessarily exert significant influence over information retrieval results within a database. Instead, in Xtube, members create alternative, activist, and interventionist forms of tags for personal and social purposes. Particularly for individuals who experience non-normative desire, such tagging practices provide a means for describing and structuring feelings of difference into coherent identities and particular social forms for socio-sexual engagement and selfexploration and understanding.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".