How Do Different Concepts of Time Further Our Understanding of Managing and Organizing Innovations?
Bibliographic record
Abstract
The symposium establishes a discussion that unfolds the role of temporality in managing and organizing for innovations. Innovations cannot be defined by a clear beginning or an end but are rather processual, nor are they linearly developing due to their temporal complexity shaped by the multiple temporal rhythms, paces, and experiences, building on both past, present, and future. By adopting an inter-temporal lens on innovations, exploring and interweaving a variety of temporal perspectives, recent studies of innovations analyze the iterative processes by which actors interpret, negotiate, and enact different combinations of past knowledge and future projections in the present in order to manage future trajectories. Each panelist will elaborate on various aspects of the emerging inter-temporal approach to innovations. Whereas, the discussion aims to bring together these perspectives reflecting on the implications that an inter-temporal view adds to the managing and organizing for innovations in different empirical fields.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.029 | 0.067 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".