A case for collaboration: solving practical problems in cultural heritage digitization projects
Bibliographic record
Abstract
COLLABORATION HAS BECOME a strategic priority at the University Library at the University of Saskatchewan, where we work. As collaboration increasingly becomes an institutional priority for other libraries and cultural heritage organizations, information professionals will be required to engage effectively in collaborative research; this chapter provides examples of and solutions to challenges posed by a wide-ranging, interinstitutional collaboration that will help to inform others who may undertake similar collaborative ventures. Many celebrate the theoretical benefits of collaboration, citing such benefits as increased resources, sharing of expertise, pooled labour effort and increased project scope (Langley, Gray and Vaughan, 2006; Miller and Pellen, 2005; Williams, 2015). With these benefits of collaboration in mind, the Saskatchewan Ministry of Education, Provincial Library and Literacy Office approached the University Library at the University of Saskatchewan with an opportunity to lead a province-wide digitization initiative that would attempt to develop a sustainable framework for dramatically increasing the amount of Saskatchewan cultural heritage content available online. This resulted in a collaborative project called Saskatchewan History Online (SHO). Increasingly professionals have been noticing another important aspect of collaboration: community. Community engagement is a key mission for SHO. William Miller (Miller and Pellen, 2005) writes, ‘there is growing under - standing of the key roles libraries have in the development of civil society, and … an obligation to enhance the integration of digital information … much more broadly throughout all levels of society’ (p. 3). Community integration with GLAMs (galleries, libraries, archives and museums) and other memory institutions, along with the ‘citizens of Saskatchewan’ (the target audience for SHO) is instrumental to the conception of the SHO project. This type of community-involved project developed within a university institution is becoming somewhat more common and certainly more strategically important, as outlined in the executive summary of Leading the Digital World: opportunities for Canada's memory institutions (Council of Canadian Academies, 2015): ‘memory institutions are beginning to realize that digital projects, which may be national or even international, must establish roots in the community in order to succeed’ (p. xiv). The many theoretical benefits to collaboration on a large-scope project gave us great enthusiasm about the potential outcomes from such an initiative. Like Anne Langley, Edward Gray and K. T. L. Vaughan (2006), we were embarking on the project with the attitude that ‘collaboration is a win-win adventure’ (p. 9).
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.028 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".