Bibliographic record
Abstract
This paper introduces the key concept ‘academobilities’ as an addition to the growing collection of keywords of mobility studies. Situating academobilities within the tradition of keywords will allow scholars across disciplines to refer to it as a tool that can be used in their own research. Academobilities is a two-fold concept. First, it calls into question the culture surrounding academia by examining the specific ways information is transported and communicated to the public, critically examining power structures, inclusions, and exclusions. The second way in which academobilities can be employed is to examine the interconnected relationship between the academy and mobility; academia is dependent upon mobility. This paper introduces academobilities as a key concept that scholars can adopt and apply in unique ways that move beyond this two-fold understanding. Scholars across disciplines can certainly add fruitful theoretical underpinnings to academobilities, andto do so is encouraged. Understandings of key concepts change and fluctuate over time (Williams 1976) to address our ever-changing society. The goal of writing this paper is to identify a starting point from which scholars of all disciplines can leap.
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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.020 | 0.051 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".