An Integrational Strategy as a Response to the Corporate-Community Engagement Challenges
Bibliographic record
Abstract
In recent years, issues related to companies' relations with their local stakeholders, namely the local community, have become increasingly salient. There is a growing consensus among practitioners, business associations, international financial institutions and consultants that these relations matter to a company's long-term success. However, little is known about how companies actually organize their internal resources to implement their community engagement strategy. Understanding these internal arrangements is timely, especially in the context of environmentally sensitive industries (e.g., mining, forestry, energy, etc.) where corporate-community relations are of tantamount importance. Based on an empirical study with 17 Canadian companies, this article opens a black box by linking the strategies of community engagement, well documented in the existing literature, to the organizational arrangements that support them. In doing so, this article contributes to the stakeholder literature by providing an in-depth examination of the experience of the very actors that are in the front line of corporate-community engagement. More specifically, it sheds light on a strategy of engagement that has been overlooked in previous works, i.e., the integrational strategy. This strategy is distinctive by its transverse and dynamic dimensions and yields tangible results for those companies that embrace flexibility.
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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".