Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the reasons for the gradual extinction of reading scholarship in Library and Information Science (LIS) departments and to identify three problematic areas accounting for its dropping prestige: paradigmatic conflicts, the influence of the corporate university and low awareness of the potential of reading research. It also proposes possible solutions to each problem. Design/methodology/approach – Close reading and analysis of an extensive selection of sources with novel conceptualization and critical perspectives. Findings – The information science paradigm, which has dominated LIS, is not sufficient to accommodate reading research. The information science model has a detrimentally restrictive effect on reading scholarship. Library science, which should be considered an autonomous discipline rather than an appendix of information science, is more conducive to the study of reading. Non-specialization-based academic hiring to increase values-based diversity in LIS through a larger influx of reading scholars is advocated. Originality/value – Reading scholarship, unduly deemed “old-fashioned”, or euphemistically “traditional”, is one of the most potent areas of academic inquiry, to which LIS scholars are perfectly positioned to make a unique contribution. Reading research in LIS has great merit irrespective of its connection to information and technology; a set of evaluative questions to determine the quality of reading scholarship is introduced. Using a case study, the paper illustrates the potential of reading research for interdisciplinary connections, community partnerships and the enrichment of LIS education and professional practices. An honest look at one of the most exciting academic fields, regrettably neglected by LIS.
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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".