Bibliographic record
Abstract
Introduction Following the presentation of our model for developing social strategy, we now turn to considering the competitive conditions conducive to corporate social strategy having a positive impact on firm performance. As should be clear from our presentation of the model, social strategy is not amenable to a one-size-fits-all approach. Rather, the formulation of social strategy must take into account both the competitive context and the unique resources and capabilities that a firm brings to the task. Scholars have argued that the development of market-based advantages is useful in less dynamic environments, while the development of capability-based advantages is vital to moderately dynamic and high-velocity environments (Grant, 1996; Teece et al ., 1997; Eisenhardt and Martin, 2000). In this chapter we will focus on how firms can achieve an advantageous market position through social strategy in different environments; in Chapter 8, we will take a closer look at resources and capabilities. The task of characterizing the competitive environment is especially challenging due to the varying ways scholars have taken up the issue (Boyd et al ., 1993). Two approaches stand out. The first is based on industrial organization economics and uses the tools of game theory to model social investment under different competitive assumptions. This approach is often applied to the macro-environment and the study, via mathematical measures, of strategic decision-making and firm behavior under norms of rationality. The second approach (introduced in Chapter 4 in our analysis of the market environment – Step 1 of Corporate Social Strategy), develops qualitative evaluations of the competitive context at multiple levels, both for the industry, and for the individual firm. This methodology depends on perceptual measures that describe the corporate decision-makers’ evaluations of the competitive environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.013 |
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".