Making them proud: Internal reputation management in the Toronto Transit Commission
Bibliographic record
Abstract
This study sought to further understanding of the impact of gaps between executive-led and front-line manager reputation management strategies through a case study of the largest public transit organisation in Canada, the Toronto Transit Commission (TTC). The study was conducted as the TTC was nearing completion of a five-year plan to improve the organisation’s reputation, including through a renewed focus on customer service delivered. At the same time, the organisation’s leadership has been engaged in high profile discussions with elected officials and opinion leaders regarding financial investment in the operating and capital needs of the organisation. Varying reputation management strategies are more effective for those publics with high proximity to an organisation than for those with low proximity, yet public service organisations like the TTC may face challenges should front line employees delivering service to clients have weak levels of coorientation with leadership. The study used semi-structured interviews with executives and managers to explore the impact of differences in perceptions of organisational reputation. The study contributes to the field of reputation management by demonstrating that: (a) employees will use concrete data for communications with stakeholders with low proximity and personalised communications for stakeholders with high proximity; (b) a coorientation analysis can provide valuable insights into the nature and impact of gaps in perceptions of organizational reputation; and (c) substantive reputation repair actions are valued by high proximity stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.000 |
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 teacher head, 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".