The Problem of Compromise in Conservation and Exhibit Decision Making
Bibliographic record
Abstract
A key challenge in managing collections is optimizing the value to society they offer, both now and in the future. This challenge can be framed as an issue requiring compromise, or, it can be considered as an opportunity to optimize. The goal is to help heritage professionals engage in constructive decision-making. By focusing on high-level institutional gains and benefits, while avoiding picking battles over less significant issues, a compromise and win-lose mindset can be avoided. The multiple objectives involved in creating a safe and effective exhibit can lead to conflict and unhelpful digging in of positions among team members. Understanding factors that contribute to conflict and identifying some means of avoiding or minimizing those factors can lead to teamwork at a higher level. Collection management challenges are explored, in a practical way, to reveal how simple changes in thinking habits and perspective can improve decisions and outcomes. A range of heuristics that shape our instinctive decision-making are explained and illustrated to create the opportunity for insight into how these unconsciously create an unnecessarily conflict-based response. Strategies for shifting perspective are discussed and offered as a route to identifying mutually beneficial outcomes.
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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 | 0.038 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".