Understanding the Qualitative Content of Relational Strategies
Bibliographic record
Abstract
Scholarship in strategic management, organization theory, and social issues in management has aimed to understand the ways in which relationships affect and are affected by organizational performance. Research on relational strategies has taken multiple perspectives. These include examining how organizations and their stakeholders engage with one another and how prior histories with exchange partners impact the outcomes of transactions. A critical part of investigating relational strategies is understanding the qualitative substance of relationships which have commonly been evaluated through quantitative indicators such as tie strength, interaction frequency, or hierarchical differences. In this Symposium, we propose the presentation of four scholarly papers, each of which examines the qualitative content of modes of relating among organizational and market actors. We envision that this dialogue will advance scholarship about how relational strategies and relationships are assessed in the organizational literature. Reframing Difficult Experiences: How Individuals Cope With Emotional Distress Author: Madeleine Stefanie Rauch; Stanford U. Author: Shahzad Ansari; U. of Cambridge Healing Deep Wounds: A Case Study of Reconciliation Between the Police and the Community in Morelia Author: Rodrigo Canales; Boston U. Shaking off the Shackles: How Colonial Institutions Impact Relational Capabilities in the Market Author: Amrita Saha; U. of Toronto, Rotman School of Management The Power of Words: Word Responses in Multimarket Competition Author: He Gao; Michigan State U. Author: Tieying Yu; Boston College Author: Hyun-Soo Woo; U. of Mississippi Author: Albert Cannella; Texas A&M U., College Station
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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".