Exploring Becoming, Doing, and Relating within the information professions
Bibliographic record
Abstract
Professional identity in Library and Information Sciences (LIS) in the United States and Canada is often defined by education, particularly the Masters in Library and Information Science(s) or its equivalent (MLIS). However, education is not the only attribute expected of an information professional. Anteby et al. (2016) developed three lenses for examining professions: Becoming, Doing and Relating. Each of these lenses provides a different view of how professional status is achieved and maintained and reflects the evolution of professional identification over the past century. Given the lack of any recognized definition within LIS, applying the lenses to “information professions” in general provides a framework to discuss professional identity. In order to understand how the LIS community defines information professional an exploratory survey was developed for information professions in the United States and Canada that included an open-ended question about professional identity. The survey was taken by more than 700 information professionals 2014–2015, and includes responses from MLIS students, information professionals with and without an MLIS (or an equivalent degree), LIS educators, retired professionals, and professionals with an MLIS working outside the field, but still active within the community. The responses uncovered a wide range of definitions, which reflected the concepts of lenses of professional status as presented by Anteby et al. However, not all of the definitions were easily assigned to a single lens. The findings do identify other important questions to consider. Why is there such a range of how we define LIS professional? What does this mean about how we interact with society in our professional roles? What impact might this have on how we are viewed by the larger society?
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".