Page i WE TAKE FROM IT WHAT WE NEED: A PORTRAITURE APPROACH TO UNDERSTANDING A SOCIAL MOVEMENT THROUGH THE POWER OF STORY AND STORYTELLING LEADERSHIP
Bibliographic record
Abstract
This study examined a social movement through the power of story and storytelling and its influence on behavior from a purposeful sampling of individuals who heard the story of Joan Southgate’s journey. Ms. Southgate, a 73-year-old African-American, walked the 519 miles of the underground-railroad across Ohio, Pennsylvania, New York and into Canada. In one sense, this grand narrative of the underground-railroad is a never-ending story but one that is subject to change with each re-telling and/or unveiling of polyphonic microstories. Stories, as no other spoken communication tool, have the ability to capture emotion and reason, hearts and minds. While storytelling is thought to be a most powerful means of communicating, very little scholarly work has been written about its use as a tool for leadership and leaders. The purpose of this study was to look at what meaning could be derived from understanding the connection between storytelling and leadership. Stories were collected and interpreted for their meaning using a social science portraiture approach, which emphasizes and respects the voice of the people being
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".