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Enregistrement W7115029020

To Teach, or Not to Teach : Who Belongs in Higher Education?

2023· article· en· W7115029020 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library of Skövde (University of Skövde) · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueAcademic Freedom and Politics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOutreachHierarchyPromotion (chess)Order (exchange)Work (physics)HousekeepingCore (optical fiber)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Swedish public universities are, by law, assigned three core duties; teaching, research, and science outreach (SFS 1992:1434, ch. 1 § 2). Legally, there is no priority among these, however, a de facto hierarchy is common in the attitude towards them (Karagiannis, 2009); in general, teaching is a necessary evil, research is prestigious, and science outreach a sporadic extra. In order to perform any of the core duties, not only is the right supportive infrastructure needed, so is performing secondary duties. Due to the implicit and supportive nature of these secondary duties, they tend to be less visible, appreciated, and rewarded. Large university functions such as administration, communication, library, and technical support, are explicitly dedicated to secondary duties, and less explicitly involved in core duties, thus they risk being taken for granted or seen as external servants. Secondary duties performed by academics can generally be categorised into internal and external services, where internal services are sometimes called academic housekeeping (Kalm, 2019) to highlight the similarities to domestic housekeeping. Such services here summarised as tasks that helps taking care of the academic family (Guarino & Borden, 2017) are generally within the school and will not entail academic rewards. External services i.e. duties such as reviewing, serving as an expert, or partaking in boards can be hard work, but the work is more visible and can also have added benefits such as payment, access to networks, and being recognised when applying for promotion (Kalm, 2019). Interestingly, research tends to have more in common with the external services, whereas teaching is more similar to internal services. It could, however, be possible to get teaching more visible and rewarding (thus more similar to external services), for instance via open educational practices (OEP, Bali et al., 2020) such as open networked learning (ONL)[1]. The academic housekeeping work is also not evenly distributed among all teachers and researchers. There is, for instance, a noticeable bias of women receiving requests for, and accepting, duties of this kind (Babcock et al., 2017; Kalm, 2019). Since these duties are less rewarded and visible, the people performing these duties are at risk of being less likely to advance their careers, or partake in the more prestigious work that their housekeeping duties facilitate. Part of the problem comes from, and is enforced by, an archaic view of academia characterised by the idealisation of the lone (research) genius, working in the isolated ivory tower (Kalm, 2019; Urai & Kelly, 2023). This leads to an us and them situation where housekeepers are locked out of the tower, and those within struggle to keep their place. Inspired by the economic doughnut model (Raworth, 2017), and slow academia (Berg & Seeber, 2016), a more holistic approach has been sketched out as a way to build a fairer and more sustainable academic culture (Urai & Kelly, 2023). Rather than considering academics as cogs in a machine chasing perpetual growth and aspiring to become a lone genius, a more apt metaphor is a garden where we all grow together (Urai & Kelly, 2023). Increasing collaboration and collegiality within teaching, research, and science outreach respectively, and also between them, will allow secondary duties to be more visible, possible to distribute, and credit, in a fairer way. Our proposition is not that everyone should do everything, but that a more complex view of people in academia than just teacher or researcher is needed. To tie education and research closer is, apart from the intrinsic values (Karagiannis, 2009; Urai & Kelly, 2023), also something explicitly requested from the faculty board (The Faculty Board, 2020). References: Babcock, L., Recalde, M. P., Vesterlund, L., & Weingart, L. (2017). Gender differences in accepting and receiving requests for tasks with low promotability. American Economic Review, 107(3), 714–747. Bali, M., Cronin, C., & Jhangiani, R. S. (2020). Framing Open Educational Practices from a Social Justice Perspective. Journal of Interactive Media in Education, 2020(1). https://doi.org/http://doi.org/10.5334/jime.565Links to an external site. Berg, M., & Seeber, B. K. (2016). The slow professor: Challenging the culture of speed in the academy. University of Toronto Press. Guarino, C. M., & Borden, V. M. H. (2017). Faculty service loads and gender: Are women taking care of the academic family? Research in Higher Education, 58, 672–694. Kalm, S. (2019). Om akademiskt hushållsarbete och dess fördelning. Sociologisk Forskning, 56(1), 5–26. Karagiannis, S. N. (2009). The Conflicts between Science Research and Teaching in Higher Education: An Academic's Perspective. International Journal of Teaching and Learning in Higher Education, 21(1), 75–83. Raworth, K. (2017). Doughnut economics: seven ways to think like a 21st-century economist. Chelsea Green Publishing. SFS. Högskolelag, 1992:1434. The Faculty Board (2020). Forskningsanknytning av utbildning. Decision of the Faculty Board, Dnr HS 2020/238, University of Skövde. Urai, A. E., & Kelly, C. (2023). Rethinking academia in a time of climate crisis. Elife, 12, e84991. [1] http://www.opennetworkedlearning.se/

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,268
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,254
Écart entre enseignants0,228 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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