Spatial thinking, gender and immaterial affective labour in the post-Fordist academic library
Bibliographic record
Abstract
Purpose The purpose of this paper is to use spatial thinking (space-time) as a lens through which to examine the ways in which the socio-economic conditions and values of the post-Fordist academy work to diminish and even subsume the immaterial affective labour of librarians even as it serves to reproduce the academy. Design/methodology/approach The research question informing this paper asks, In what ways does spatial thinking help us to better understand the immaterial, invisible and gendered labour of academic librarians' public service work in the context of the post-Fordist university? This question is explored using a conceptual approach and a review of recent library information science (LIS) literature that situates the academic library in the post-Fordist knowledge economy. Findings The findings suggest that the feminized and gendered immaterial labour of public service work in academic libraries – a form of reproductive labour – remains invisible and undervalued in the post-Fordist university, and that academic libraries function as a procreative, feminized spaces. Originality/value Spatial thinking offers a corrective to the tendency in LIS to foreground time over space. It affords new insights into the spatial and temporal aspects of information work in the global neoliberal knowledge economy and suggests a new spatio-temporal imaginary of the post-Fordist academic library as a site of waged work.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".