The Place of Inspiration in Heritage Interpretation: Views From the Field
Bibliographic record
Abstract
While the idea of inspiration spans the history of the field, inspiration as a topic has been an under-studied phenomenon in the interpretation field. Thirty-three professional interpreters from 21 agencies across North America participated in dialog on the topic of inspiration in interpretation through Internet-based World Café sessions. The dialog was rich and thoughtful, and revealed that these interpreters believed that inspiration is the goal of interpretation and that the nine characteristics of inspiration identified in the literature and reviewed in our prior publication were relevant to interpretation: that inspiration is two-sided, transmissible, positive, individual, transcendent, unexpected, holistic, requires receptivity, and receptivity may be cultivated. Identified barriers to using an inspiration-based approach in interpretation included meeting the needs of agencies, and particularly the agency’s needs for quantitative measurements of attendance and impacts.
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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 | 0.050 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.087 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.016 |
| 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".