Bibliographic record
Abstract
Hashtagging photos on Instagram represents a recent iteration of user-generated content organization. This thesis examines whether established theories of classification can explain these hashtagging practices by asking the questions "Are early conceptions of user generated classifications useful descriptors of hashtagging practices on Instagram?" And "Do older classification theories, developed prior to hashtagging practices still apply in a user generated context?" The first examines nonformal classification systems such as folksonomies while the second examines Hacking's (1986, 1996) dynamic nominalism, Bowker and Stars' (1999) case study informed definitions, and perspectives on the role of ontologies in classification. A hybrid walkthrough methodology was applied in Instagram to empirically examine the classificatory processes of content producers for thirty-five sets of hashtag pools of photos. Overall most characteristics of formal classification systems apply to Instagram user generated content and unsurprisingly, hashtagging practices on Instagram are best characterized as folksonomic.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| 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".