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Record W7115029020

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

2023· article· en· W7115029020 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library of Skövde (University of Skövde) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic Freedom and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachHierarchyPromotion (chess)Order (exchange)Work (physics)HousekeepingCore (optical fiber)
DOInot available

Abstract

fetched live from 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/

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)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.023
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0160.017
Scholarly communication0.0160.015
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0160.004

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 head0.026
GPT teacher head0.254
Teacher spread0.228 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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