Academia’s Ivory Tower within the Worlds of New Media and Popular Culture
Bibliographic record
Abstract
This paper focuses on issues of the accessibility and approachability of the aca-demic space, the ways it is generally represented outside of academia as well as how scholars who have written about being academics perceive it. It discusses the levels of inclusion and exclusion that are present when academia is seen in rela-tion to the real world and what both of these states generate in regard to the act of forming opinions of higher education and its usefulness in the eyes of the general public. Transcendingthe boundaries of academia, this paper explores how grad-uate students who are a part of academia attempt to deal with the clash of different identity points and mental health problems caused by it, and also how they try to forward academia into new spacessuch as popular culture, music, or social media. These include for instance the American rapper Sammus, or the Canadian theo-retician Kristen Cochrane. This paper further delves into ways in which academic space and university experience are represented onsocial media entertainment platforms as well as the means by which universities promote themselves online. In this regard this paper’s aim lies in searching for a defamiliarized view of aca-demia and creating a pathway for making its ivory tower more down to earth. This paper concludes that by getting closer to audiences with broader sets of interests, academia has an increasingly better chance of gaining new meanings, which may ultimately prove beneficial for the understanding of its significance not only within the sector of education, but also outside of it.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.066 |
| Scholarly communication | 0.034 | 0.021 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".