From Elements to Structures: An Agenda for Organisational Gamification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gamification is gaining popularity in organisational settings, yet it is unclear if investments in organisational gamification will pay off, given that reports of mixed results are commonplace in the literature. It is important that potential factors behind any mixed results from the initial wave of gamification research be identified and addressed before organisational scholars and practitioners start investing valuable resources into large-scale gamification projects. In this Issues and Opinions paper, we identify and discuss several reasons that may be contributing to the problem of mixed results. We ground our arguments in an umbrella review of the gamification literature. In line with the theme of “Putting more than mere ‘Fun and Games’ into Systems” for this special issue, we propose a framework grounded in Adaptive Structuration Theory and present a set of research questions that can help guide future organisational gamification research. Further, based on the strengths and limitations of our work, we identify several additional avenues to stimulate future research and produce fresh practical insights.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it