Painful memories as mnemonic resources: Grand Canyon Dories and the protection of place
Bibliographic record
Abstract
Organizations commonly regard memories of pain and destruction as being unwanted. In this article, we consider the largely undertheorized potential that painful pasts can have for building a mnemonic community. We draw primarily on oral history interviews to explore how Martin Litton and Grand Canyon Dories use sensory, discursive, and material-discursive modalities to convert painful memories into mnemonic resources through the performance of three practices: sensitizing, retelling, and reincarnating. Their aim was to protect the Grand Canyon for future generations. We advance research on organizational uses of the past by theorizing how painful memories can be converted into mnemonic resources. Specifically, we underscore the untapped potential of organizations repackaging history-at-large to curate experiences of the past using combinations of semiotic modalities and remembering practices. We call this multimodal remembering. We also contribute to research on place by illustrating how destroyed natural wonders that no longer exist in their geological corporeal form can be transposed across time and space and become re-embodied in new phantasmatic forms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".