Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the information behaviour of early career academics (ECAs) within humanities and social sciences (HSS) disciplines who are starting their first continuing academic position. The proposed grounded theory of Systemic Managerial Constraints (SMC) is introduced as a way to understand the influence of neoliberal universities on the information behaviour of ECAs. Design/methodology/approach This qualitative research used constructivist grounded theory methodology. Participants were 20 Australian and Canadian ECAs from HSS. Their information practices and information behaviour were examined for a period of five to seven months using two interviews and multiple “check-ins”. Data were analysed through two rounds of coding, where codes were iteratively compared and contrasted. Findings SMC emerged from the analysis and is proposed as a grounded theory to help better understand the context of higher education and its influence on ECAs’ information behaviour. SMC presents university managerialism, resulting from neoliberalism, as pervasive and constraining both the work ECAs do and how they perform that work. SMC helps to explain ECAs’ uncertainty and precarity in higher education and changing information needs as a result of altered work role, which, in turn, leads ECAs to seek and share information with their colleagues and use information to wield their personal agency to respond to SMC. Originality/value The findings from this paper provide a lens through which to view universities as information environments and the influence these environments can have on ECAs’ information practices and information behaviour.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".