Exploring the Textual and Tactile Weave of Academic Subjectivities:
Bibliographic record
Abstract
Within the neoliberal university, academics become positioned around market-driven managerialist ideologies and the techniques enacting those principles. Audit cultures actively and continuously measure and shape academic subjectivities defined by a specific kind of success. The market-driven individualistic model can conflict with ethical ideals and longings for self-expression, while the mismatch between institutional goals and personal values creates an academic self that is pulled in conflicting directions. We become subjects of the discourse but we can limit our subjectivity and develop authentic insight. In this paper, we engage in a process of embodied making. We create textual and tactile self-portraits as a way of pushing back at neoliberal subjectivities, and to make visible our multiple selves. Although we recognize that we are always a part of what we resist, we use making as a way to create micro-resistances for our own renewal. Our self-portrait assemblages and stories are ambiguous and fluid but capture a view of selves hidden beneath the professional self. We are reminded that we are creative beings, and that there is room within neoliberalism to open intentional spaces. We may not always succeed in seeing our contradictory identities, yet we are able to occasionally capture glimpses of our shifting selves.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".