Fandom, food, and folksonomies: The methodological realities of studying fun life‐contexts
Bibliographic record
Abstract
ABSTRACT As Library and Information Science research has evolved, new domains of interest have shaped the field, and with them comes a need to question the appropriateness of applying traditional methodologies to these new domains. This panel focuses on the methodological realities of studying fun life‐contexts and will address how researching a new domain comes with challenges and opportunities. The group of scholars on this panel all share an appreciation for identifying and exploring the unique information experiences within fun life‐contexts, and engage with a variety of subfields, including information behavior, information organization, embodied information, and fan communities. This interactive panel will consist of five short presentations from each of the panelists and a moderated Q&A led by moderator, Jenna Hartel. The panelists each share some examples of their recent work studying fun life‐contexts, reflect on their experience researching in a new domain, and develop themes and questions that should be addressed in future work.
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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 | 0.048 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".