Bibliographic record
Abstract
Purpose The purpose of this paper is to propose an evidence-based environmental scanning model that will provide a methodological framework for conducting community-engaged and community-focused research, with a particular emphasis on northern communities in Canada. Design/methodology/approach The study has adopted a multifaceted environmental scanning approach to understand the Inuvialuit Settlement Region communities. The research design is informed by various environmental models as discussed in literature from a broad range of domains such as business, library and information science (LIS), and a sophisticated multimethod data gathering approach that included field trips, observations, surveys, as well as informal methods of community engagement. Findings The paper proposes an environmental scan model as a novel approach to community-focused digital library (DL) development. The paper identifies both macro- and micro-environmental landscapes as applicable to the development of a DL for communities in Canada’s North. The macro-environmental landscapes include: geographical, historical and sociocultural, political and regulatory, economic, technological, competition, and human resource. The micro-environmental landscapes include: stakeholder and community, linguistic, information resource, and ownership. Originality/value The environmental scanning model and its key components presented in this paper provide a novel and concrete example of a project that aims to organize information for increased access and to create value through the design and implementation of an infrastructure for a cultural heritage DL. The environmental scan model will also contribute to both research and practice in the field of Library and Information Science (LIS), particularly in the area of DL development for rural, remote, and indigenous communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.021 |
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".