A Value Co-Creation Perspective on Faculty Staffing
Bibliographic record
Abstract
Colleges and universities increasingly employ temporary instructors. Researchers in higher education have voiced strong concerns about this trend because of its impact on educational outcomes, operations of academic institutions, and the composition of academic workforce. To enhance our understanding of this employment practice, this article makes three contributions to the research on the growing non-tenured employment in academia. First, we advance the theory by arguing that the value co-creation framework, also referred to as service science, is an appropriate theoretical lens for studying higher education, including faculty employment. Second, we use this framework to analyze operations of a selective undergraduate program in Finland that has been functioning for over 30 years without permanent teaching faculty. Housed at a premier business school, the program relies on an expansive international network of instructors who travel to teach on short-term contracts. Third, we demonstrate that the staffing model used by this Finnish program is distinct from other forms of temporary academic employment, and therefore we label it a networked faculty staffing model. To the best of our knowledge, this is the first time the value co-creation framework is used as a theoretical lens to study employment in higher education. Moreover, this is the first time a networked faculty staffing model is explicitly identified and described. Besides researchers, this article might be of interest to the diverse international audience who are involved in management and policy setting in higher education.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".