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 following two 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 question 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 Gruber (2007) and Iliadis' (2016) 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 hashtagged pools of photos. Overall most of the characteristics of formal classification systems and theories apply to Instagram user generated content and unsurprisingly, hashtagging practices on Instagram can best be characterized as being folksonomic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".