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Record W2595592080

Information in Transition : Examining the Information Behaviour of Academics as they Transition into University Careers

2016· dissertation· en· W2595592080 on OpenAlexfundaboutno aff
Rebekah Willson

Bibliographic record

VenueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaCharles Sturt University
KeywordsTransition (genetics)Grounded theoryCritical discourse analysisSociologyPedagogyPublic relationsSocial sciencePolitical scienceQualitative research
DOInot available

Abstract

fetched live from OpenAlex

Transitions are often times of upheaval. A transition, even when<br/>positive, may be disruptive as familiar contexts, supports, and resources change.<br/>While early career academics are highly trained and experienced, the transition<br/>from doctoral student to academic involves a series of new roles and<br/>responsibilities within a new information environment, an environment that has<br/>been influenced by neoliberal ideals and become increasingly corporatised and<br/>managerial in nature. Within information behaviour research there has been a<br/>lack of research that focuses specifically on periods of transition, particularly<br/>on individuals in transition over time. Additionally, while there is information<br/>behaviour research on academics, it does not address the experiences of<br/>academics as they start their careers. This research addresses those gaps.<br/><br/>This research used constructivist grounded theory and critical discourse<br/>analysis as methodologies to explore the information behaviour of 20<br/>individuals transitioning from doctoral students to academics in Australia and<br/>Canada. Academics in the humanities and social sciences, who had recently<br/>moved from full-time doctoral studies to full-time academic positions, were<br/>followed for a period of between five and seven months. To triangulate the data,<br/>three data sources were used: two in-depth interviews, multiple check-ins, and<br/>documents. Interviews were analysed using grounded theory analysis,<br/>documents using critical discourse analysis. Two theoretical frameworks were<br/>used to provide analytical lenses: neoliberalism and Transitions Theory. Several<br/>major themes emerged from this research that contribute to both information<br/>behaviour research and Transitions Theory.<br/><br/>In looking at academics’ work, the number and variety of administrative<br/>and managerial tasks universities require academics to perform greatly<br/>increases their information needs. Administrative work becomes a layer over all<br/>academic work. However, universities frequently fail to provide the information<br/>academics require, leaving information needs unfulfilled. Because of this, early<br/>career academics frequently seek information from their more senior colleagues,<br/>rather than relying on textual sources. Senior colleagues provide timely,<br/>convenient, and comprehensive information. Physical proximity and the<br/>building of collegial relationships promote information sharing, informal<br/>information exchanges, and serendipitous information finding that is of great<br/>use to early career academics. Social information is instrumental for early<br/>career academics’ settling in to their new positions, as doctoral studies often fail<br/>to provide an accurate picture of academic life or to fully prepare students for<br/>research, teaching, service, and administrative roles. Comparing and contrasting<br/>previous experiences to their current experience is one way that early career<br/>academics use new information to learn new ways of working and develop a<br/>sense of belonging in academia. From these findings, the theory of Systemic<br/>Managerial Constraints (SMC) emerged. SMC views the managerialism that<br/>results from neoliberalism within universities as pervasive and constraining<br/>both what work early career academics do and how they do it. However,<br/>colleagues help to ameliorate the effects of SMC and early career academics<br/>learn, as they transition, to enact their personal agency to enable them to do the<br/>work that they value.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.003
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.011
GPT teacher head0.215
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde)Same topicLibrary Science and Information LiteracyFrench-language works237,207