To Teach, or Not to Teach : Who Belongs in Higher Education?
Bibliographic record
Abstract
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/
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".