Contribution of information professionals in the multidisciplinary world of web information systems (WIS)
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
Abstract This paper presents the results from an analysis of the tasks of information professionals pertaining to Web information systems (WIS). This research was based on information professionals working in seven departments of the Canadian federal government. This study offers a better understanding of the information professionals' role in WIS, in the context of an organization heading toward becoming “digital”. A qualitative content analysis was completed on the basis of 32 interviews conducted with information professionals involved in WIS. The results indicate that information professionals are performing tasks that can be grouped in four categories–content, technology, users, graphic interface. The predominant tasks are those related to the content, although the technological tasks and the WIS management task are also very present. Three factors were identified that have an impact on the involvement of information professionals in WIS: (1) the types of WIS, (2) the organizational levels represented by WIS, and (3) the types of positions filled by the information professionals.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.001 | 0.000 |
| 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