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Record W3012545596 · doi:10.3390/soc10010026

Tackling Complex Social Challenges within Neoliberal Constraints: The Context Shaping ‘Intellectual Quality of Life’ (iQoL) in a Canadian University Context

2020· article· en· W3012545596 on OpenAlexaffabout
Suzanne Huot, Jocelyn McKay, Skye Barbic, Alison Wylie, Dominique Weis, Sarah Sherman, Liisa Holsti

Bibliographic record

VenueSocieties · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)SociologyPublic relationsConstruct (python library)Political scienceEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

The contemporary academic environment in Canada has undergone reorganization based on neoliberal principles, and has increased attention focused on the importance of supporting interdisciplinary initiatives to address complex problems affecting global society. The purpose of our study was to examine the experience of people participating in a specific university-funded interdisciplinary research initiative. As there is a strong emphasis within this program on reporting on the outcomes of the funding that supports interdisciplinary collaboration, our aim was to explore how participation may shape one’s intellectual quality of life (iQoL) and how one’s iQoL could be conceptualized and understood. Using a pragmatic constructivist case study, focus group and individual interviews were undertaken with 30 participants involved with university-funded interdisciplinary research teams. Findings illustrate that their iQoL was shaped by their capacity to engage in and achieve what they viewed as their core work and its outcomes. Related sub-themes addressed the social and relational climate, institutional environment and structure, and expectations and resources. We argue that further development of iQoL as a unique construct is required to adequately measure the full range of people’s experiences in academia, particularly when aiming to address ‘wicked’ social and global problems within a predominantly neoliberal context.

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)0.010
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0340.039
Scholarly communication0.0140.003
Open science0.0030.016
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.344
GPT teacher head0.399
Teacher spread0.056 · 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.

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

Citations3
Published2020
Admission routes2
Has abstractyes

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