Collapsed publics: Orality, literacy, and vulnerability in academic Twitter
Bibliographic record
Abstract
This paper outlines ways in which scholars build identity and connection on open networked platforms such as Twitter, and considers the risks and benefits of networked participatory engagement. The paper reports the findings of an ethnographic study examining the digitally-networked practices of scholars from a range of disciplines, identity positions, and geopolitical locations, and explores participants’ experiences of care and vulnerability within open, networked academic systems. The paper draws on White and LeCornu’s (2011) visitors and residents continuum, Veletsianos and Kimmons’ (2012) concept of Networked Participatory Scholarship (NPS), and Ong’s (1982) theories of secondary orality and secondary literacy to explore networked scholars’ practices and experiences. It examines ‘academic Twitter’ as a phenomenon in which oral and literate traditions – and audience expectations – are collapsed, creating a public that operates on very different terms from those of academia. The paper’s findings examine the risks of this collapse, yet also show that networked engagement – in which personal identity signals, humor, and expressions of commonality are found to be the dominant means by which scholars build networks ties – can result in opportunities and affinities that institutional scholarship may not offer. The substantive goal of the paper is to offer a portrait of networked scholars’ experiences and practices related to engagement, and to consider the tensions these practices raise within the contemporary academy.
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 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.023 |
| Research integrity | 0.002 | 0.003 |
| 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".