Tracing Trajectories: Analyzing and Visualizing Unfolding Sequences of Action
Bibliographic record
Abstract
Scholars are paying increasing attention to trajectories of action as a way to study how organizational process emerge, change, and evolve. Trajectories include both the sequences of action that unfold over time as well as the interactions that shape the course of those actions. Although a trajectory approach is a powerful way to examine organizational processes, it is often difficult to collect the fine-grained process data necessary to construct trajectories, and it can be challenging to analyze and visualize trajectories. In this symposium, we argue that trajectories are a generative but underused approach for examining organizational processes. We bring together a group of scholars with expertise in trajectory-based research to present some of their own research, with the hope that this set of presentations will act as a jumping off point for a broader discussion of how studying trajectories might enhance our knowledge of organizational processes. Managing a real-world unexpected event: Trajectories of routine and non-routine activities Presenter: Marlys K. Christianson; U. of Toronto Presenter: Serena Sohrab; U. of Ontario Institute of Technology Presenter: Mary J. Waller; Texas Christian U. Fluid and stable mapping: Trajectories of team action patterns and adaptive outcomes Presenter: Sjir Uitdewilligen; Maastricht U. Presenter: Mary J. Waller; Texas Christian U. Presenter: Ramon Rico; U. of Western Australia Behind the curtain: Tracking the nested trajectories of stage management teams Presenter: Marzieh Saghafian; Schulich School of Business Presenter: Mary J. Waller; Texas Christian U. Presenter: Seth A. Kaplan; George Mason U. Presenter: Wendy Reid; HEC Montreal Visualizing clinical trajectories with ThreadNet Presenter: Brian T. Pentland; Michigan State U.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".