Bibliographic record
Abstract
Will collaboration for digital security survive new challengers? The speed and disruptive character of digital innovations affect social structures and practices faster than institutions can keep up with them. This results in an ‘institutional void’, i.e. a gap between the rules and institutions and their ability and the effectiveness of their measures. It also affects the institutional stability that is the basis for the paradigm of collaboration-based types of governance. In this paper, we explore how parties are able to set up collaboration for digital security, which is inherently a topic that transcends organisational boundaries. Yet digital innovations constantly enable new challengers that might not share the same incentives for collaboration. Life in an institutional void is convenient for them and enables new business models. Hence, a key question is whether (institutionalised) collaboration is a sustainable model for addressing shared problems like digital security. We explore this question in the domain of financial cyber fraud. The new (regulatory) space currently being created for innovators suggests that the answer is ‘no’. It is too early to say how this will play out specifically and we argue for further research into the antecedents for collaboration in institutional voids.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".