From Elements to Structures: An Agenda for Organisational Gamification
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.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.023 | 0.036 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".