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Record W3208540566 · doi:10.11575/ajer.v67i2.70164

Neoliberal Sleight of Hand in a University Strategic Plan: Weaponized Sustainability, Strategic Absences, and Magic Time

2020· article· en· W3208540566 on OpenAlexaffabout
Sandra G. Kouritzin, Satoru Nakagawa, Erica Kolomic, Taylor Ellis

Bibliographic record

VenueUniversity of Calgary · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStrategic planningMandateWorkloadSustainabilityMAGIC (telescope)Context (archaeology)Plan (archaeology)Political scienceSociologyPublic administrationPublic relationsManagementEconomicsLawGeography

Abstract

fetched live from OpenAlex

This research analyzed the University of Manitoba (UofM) Strategic Plan to both quantify the occurrence of specific keywords and make inferences about the purpose, messages, and effects being communicated by those keywords. In the first section of this paper, we provide an overview of the historical development and purpose of the neoliberal university. Second, we engage with content and critical discourse analysis to understand how academic work and academic identities are established by the strategic plan, a document that few faculty members consult regularly, and fewer still have a hand in developing. The foundational ideals of the UofM, cited at the beginning of the Strategic Plan are tracked through the document, and keywords as determined by frequency count are then similarly tracked. The findings reveal that most of the foundational ideals are not well-represented in the strategic plan; others, like sustainability, are euphemized. Keywords related to increasing workload are found over 60 times in the document, all in the context of decreased funding. We conclude that the strategic plan is a clear mandate for more to be completed with less. We refer to this aspect of workload creep as the need for “magic time.” Key words: workload creep, magic time, neoliberalism, higher education, keyword analysis. Cette recherche a analysé le plan stratégique de l'Université du Manitoba afin de quantifier l'occurrence de mots-clés spécifiques et de faire des inférences sur l'objectif, les messages et les effets communiqués par ces mots-clés. Dans la première section de ce document, nous présentons un aperçu du développement historique et de l'objectif de l'université néolibérale. Ensuite, nous nous engageons dans une analyse du contenu et du discours critique afin de comprendre l’établissement du travail et des identités universitaires par le plan stratégique, un document que peu de membres du corps enseignant consultent régulièrement, et à l'élaboration duquel encore moins participent. La présence des idéaux fondateurs de l'Université du Manitoba, cités au début du plan stratégique, a été suivie tout au long du document et l’emploi des mots-clés, déterminés par le comptage de fréquence, a ensuite été suivi de la même manière. Les résultats révèlent que la plupart des idéaux fondateurs ne sont pas bien représentés dans le plan stratégique ; d'autres, comme la durabilité, sont euphémisés. Les mots-clés liés à l'augmentation de la charge de travail se retrouvent plus de 60 fois dans le document, tous dans le contexte d'une diminution du financement. Nous en concluons que le plan stratégique est un mandat clair pour que l'on fasse plus avec moins. Nous appelons cet aspect de l'augmentation de la charge de travail le besoin de « temps magique ». Mots-clés: progression de la charge de travail, temps magique, néolibéralisme, enseignement supérieur, analyse des mots clés

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)0.016
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 score0.995
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0050.009
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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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