Bibliographic record
Abstract
The formulation of alliances and partnerships is a global trend that is growing at an exponential rate. In the United States, alliances now account for 18% of the revenue of Fortune 1,000 Companies—and this figure is expected to exceed 30% by 2004. In Europe, alliances are growing at an even faster rate, and already represent over 30% of revenue. According to recent surveys, 82% of United States executives believe alliances will be a prime vehicle for future growth, and managing alliances is consistently mentioned as one of their three biggest challenges. Developing a competence in alliances and other collaborative arrangements, therefore, is now high on virtually all corporate agendas. Yet the ability to successfully manage alliances remains elusive. If current trends continue, about 70% of all alliances will fail to deliver the expected results. In most cases, failure is attributed to mismatches in corporate culture, poor communications, or some similarly high-level cause. This conventional analysis camouflages some specific and fundamental capabilities that are critical for alliance success. These capabilities address facilitating and maintaining alliance-like thinking and behaviours that are a match for alliance strategies. The ability to develop the appropriate thinking and behaviour to be a valued partner is a distinct corporate competitive advantage. Using recent examples in the oil and gas industry in Canada and Australia, this paper details three key capabilities that are critical to alliance success. Some new approaches to effective partnering in any environment or industry are offered, to help in reframing the challenges that inevitably arise.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".