On the Space/Time of Information Literacy, Higher Education, and the Global Knowledge Economy
Bibliographic record
Abstract
Local sites and practices of information work become embroiled in the larger imperatives and logics of the global knowledge economy through social, technological, and spatial networks. Drawing on human geography’s central claim that space and time are dialectically produced through social practices, in this essay I use human/critical geography as a framework to situate the processes and practices—the space and time—of information literacy within the broader social, political, and economic environments of the global knowledge economy. As skills training for the knowledge economy, information literacy lies at the intersection of the spatial and temporal spheres of higher education as the locus of human capital production. Information literacy emerges as a priority for academic librarians in the 1980s in the context of neoliberal reforms to higher education: a necessary skill in the burgeoning “information economy,” it legitimates the role of librarians as teachers. As a strategic priority, information literacy serves to demonstrate the library’s value within the university’s globalizing agenda. While there has been a renewed interest in space/time within the humanities and social sciences since the 1980s, LIS has not taken up this “spatial turn” with the same enthusiasm—or the same degree of criticality—as other social science disciplines. This article attempts to address that gap and offers new insights into the ways that the spatial and temporal registers of the global knowledge economy and the neoliberal university produce and regulate the practice of information literacy in the academic library. Pre-print first published online 12/09/2018
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.059 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".