مشاغل نوین مبتنی بر فناوریهای اطلاعات برای فارغالتحصیلان علوم اطلاعات و دانششناسی در عصر حاضر
Bibliographic record
Abstract
Objective: This study examines the impact of information technologies on new library and information science job opportunities and the content analysis of LIS job advertisements.Methodology: The approach of the current research is qualitative and its methodology is content analysis. In a purposeful sampling, 276 published job advertisements from USA and Canada in first 6 month of 2013 in indeed.com job searching website were selected and analyzed.Findings: 95 new jobs detected from 276 job advertisements related to positions of information technology areas.Findings showed that totally, four job types of “Systems librarian”, “Metadata librarian”, “E-Resources Librarian” and “Web librarian” were gained three quarters of job advertisements. In 71 percent job advertisements, it was nescessary for the applicants to have a master degree (Library Science, Information Science, Library and Information Science) or an equivalent degree accredited by American Library Association (ALA).The analysis of syllabus approved by “Ministry of Science, Research and Technology” for educating librarian shows that only 18 credits of the syllabus are IT Competency-based.Keywords: Web Librarian, Systems Librarian, Curriculum, Job Advertisments, Job Market
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".