Diversity, Equity, Inclusion, and Information Systems : A qualitative study on how DEI teams in Canada interact with information systems
Bibliographic record
Abstract
Diversity, equity, and inclusion are concepts that have been embraced by organizations in the past years. More and more, companies understand the need to leverage diversity, equity and inclusion in their workforce, and a group of professionals have emerged to support them to achieve this goal: the DEI teams. These workers play a role in advising organizations about the best talent management practices that support a DEI friendly work environment. The Information Systems (IS) field has also studied how organizational structures interact with information systems and how they impact each other. Therefore, it is also necessary to learn about the DEI professional’s perspective on how their work in the organizations interact with information systems and technologies. Moreover, the DEI concepts and the work of DEI professionals have become popular in Canada in the past years, however, there has not been enough research in the IS field on how information systems impact diversity, equity, and inclusion in the workforce. For this reason, this research aims to contribute to the IS field by adding some piece of knowledge regarding the interaction between the work of the DEI professionals and information systems. Following a qualitative research approach, some interview was conducted with eleven DEI professionals from different organizations and job positions in Toronto, Canada. The interviews focused on their experience and perception of how information systems can support or impact their efforts to leverage diversity, equity, and inclusion in their organizations’ workforce. The data collected during the interview was analysed which led to seven main concepts. The results have shown a positive perception of the DEI professionals in Canada regarding the use of network or social media platforms such as LinkedIn and Facebook in helping organizations to leverage DEI in their workforce. The DEI professionals’ focus on targeted recruitment also has shaped their interactions with the information systems. However, the participants shared some concerns regarding the quality of the data collected from self-identification forms as well as the use of the automated cv screening tools.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".