Projecting and Innovating for the Future: Tackling Grand Challenges with Projects
Bibliographic record
Abstract
How can organizing for projects contribute to organizing for grand challenges? This is the question at the core of this presenter symposium which invites project scholars and management scholars to contribute to tackling grand challenges with projects. The symposium is proposed as a forum or “stage setter” to open up new areas of management inquiry on the topic of organizing projects to tackle grand challenges. It is preceded and followed by special issues in different journals including International Journal of Project Management and Journal of Operations Management, respectively. Tackling grand challenges with projects Author: Lavagnon A. Ika; U. of Ottawa Author: Dror Etzion; U. of Vermont, Grossman School of Business, US Author: Elliot Bendoly; Ohio State U. OSCM, grand Challenges, and projects Author: Tyson Browning; Texas Christian U. Author: Anant Mishra; U. of Minnesota Creating and distributing social value from projects to tackle grand challenges Author: Jens Roehrich; U. of Bath Author: Ofer Zwikael; Australian National U. Net-zero projects and new ways of organizing Author: Giorgio Locatelli; Politecnico di Milano School of Management ESG integration in major projects: Navigating complexity for sustainable development Author: Nathalie Drouin; UQAM U. of Quebec in Montreal, Canada
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.
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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.030 |
| Scholarly communication | 0.034 | 0.036 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.007 | 0.008 |
| 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".