LARRY J. HACKMAN, ed., Many Happy Returns: Advocacy and the Development of Archives
Bibliographic record
Abstract
In Many Happy Returns: Advocacy and the Development of Archives, editor and former New York State archivist Larry J. Hackman argues persuasively for considering advocacy a core archival function, as central to archival practice as appraisal or arrangement and description.While the term "advocacy" brings to mind the efforts by archival associations or individuals in support of archival-friendly legislation or government funding, for the purposes of this volume it refers to the advocacy work that archivists (should) do on behalf of their own programs.Hackman defines advocacy as "activities consciously aimed to persuade individuals or organizations to act on behalf of a program or institution" (p.vii).More than fifteen years have elapsed since the publication of the manual Advocating Archives: An Introduction to Public Relations for Archives. 1 This new publication expands on the previous manual both in terms of the definition of advocacy and the exploration of the topic.Indeed, Many Happy Returns is very comprehensive.Part One is an essay by Hackman in which he outlines a series of advocacy principles, as well as suggested strategies and approaches.Part Two consists of thirteen case studies that describe successful advocacy projects in a good, but not perfect, cross-section of archival institutions, including special collections departments of university libraries, independent arts organizations, and state and municipal archives.Unfortunately archives that collect corporate records of universities or religious institutions are not represented in the case studies.Part Three comprises a series of essays examining "perspectives on advocacy," including the implications for archival 1 Finch, Elsie Freeman, ed.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".