Consultants in Academic Libraries: Challenging, Renewing, and Extending the Dialogue
Bibliographic record
Abstract
There is a trend in academic libraries to hire consultants for internal crises, change management projects, strategic planning processes, outcomes assessment, evidence-based decision making, information literacy instruction, and more. Although we hear informally about the use of consultants in academic libraries, the practice has gone unexamined. We employ a historical and linguistic analysis of consultants in academic libraries, using a critical framework for this research. A critical perspective provides a structure to discuss issues that librarians may not have been able to previously fit into library practice dialogue. A chronological history of consulting in libraries acts as our literature review. This review, along with a targeted examination of library and information science resources, is used to guide two lines of linguistic analysis. The first provides a critique of the core tenets used to define and characterize library consultants, namely, the claim that consultants are unbiased professionals who bring “expertise” and “fresh” ideas to libraries. The second analysis investigates the rhetorical strategies used in existing texts: polarizing language, straw man reasoning, and figurative and indirect language. The discussion section unpacks these linguistic strategies, reflects on what is missing from the texts, and considers how knowledge and power are exerted through language, making connections to the broader context of neoliberalism.
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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 | 0.078 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.042 | 0.094 |
| Scholarly communication | 0.059 | 0.049 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".