Bibliographic record
Abstract
In this paper, we argue that the crisis of teaching can be understood as a crisis of labour that continues to impact academic librarians because it is a historical process grounded in larger socio-political shifts precipitated by capitalism. We demonstrate that the emergence and development of teaching—and specifically teaching information literacy (IL) as a kind of librarian curriculum—in academic libraries in North America corresponds to the emergence of neoliberalism. The shocks created by neoliberal fiscal austerity along with anxiety about de-professionalization and de-skilling provoked by cheaper and more widely available information technology created a mounting crisis of legitimacy in librarianship throughout the late 1970s and into the 1980s. Librarians ostensibly remedied this crisis through the positioning of IL as a central contribution of the profession to the academy and society. The COVID-19 pandemic and economic recessions have only intensified the proletarianization processes that have been ongoing since the 1970s. As teaching, learning, and assessment technologies proliferate in the academy, librarians cannot teach more efficiently to meet the needs of growing university populations. Instead, they must rethink the purpose and goals of librarian teaching in the context of the academy. The question of teaching will not be solved until material conditions of librarian labour in the academy are solved.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.057 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".