Capturing digital developments through qualitative inquiry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to describe a model of digital library (DL) work that surfaced through the ARL Profiles 2010 and resonates current work underway by the large-scale DL projects like DPLA, SHARE, Hathitrust, Academic Preservation Trust, and Digital Preservation Network. Design/methodology/approach – In total, 86 ARL members submitted institutional profiles that were analyzed using Atlas.ti and surfaced major themes that comprise the mission of research libraries including serving the public good, expanding their presence globally, setting standards for access and quality, needing to explore best practices, and being visible at the national and international levels. Findings – The analysis of the narratives identified three key areas for DL developments: first, digitized special collections, second, acquiring digital content, and third, developing digital services (Figure 1). Specific examples and context are provided in the paper. Research limitations/implications – The qualitative data collected from these profiles demonstrate that libraries are transforming their services to leverage digital technologies and meet the changing needs of their users. The approach was open ended and allowed libraries to celebrate their strengths and unique context. Some of the disadvantages of this approach include the amount of work both on behalf of the participating libraries as well as on behalf of the analysts and the difficulty of comparing libraries with one another. Originality/value – This was the first time ARL attempted to describe research libraries using narrative descriptions. The approach complements the traditional ARL Statistics and offers a viable alternative in a future environment that is dominated by radical change. This qualitative approach deployed here is critical for describing DL developments as current large-scale digitization projects have extended the directions that were surfacing in these profiles and have major implications for the future of digital content and research. Future research in this area is strongly encouraged.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.037 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it