Bibliographic record
Abstract
Economic impact analyses have not been widely conducted in archives. This article reports on a two parallel surveys in the US and Canada to assess the economic impact of government archives (state, provincial, territorial, county, and municipal). The surveys utilize indirect measures of economic impact. Responses from 2,534 people in 66 archives were analyzed. Findings indicate that archives were the primary reason that respondents visited an area and that visitors exhibit specific patterns of visiting the archives in conjunction with other cultural organizations in an area. Furthermore, while many respondents used local eateries, fewer rented lodgings or spent money on theater or sporting events. As a result, the archives participating in this survey did have a modest impact on local economies. The article concludes by discussing three major questions about the evaluation of the economic impact of archives which were raised by the findings: 1) Are indirect measures the most appropriate means of assessing economic impact in archives or should archives employ direct measures as used by public libraries? 2) How should government archives formulate their value proposition and should those value propositions focus on other aspects of archives’ impact, such as the social impact, to demonstrate archives’ important role in society? and 3) Since visitors exhibited distinct visitation patterns, should archives work more with these other aligned organizations and work on larger forms of collective impact that benefit the entire cultural heritage sector in an area?
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".