THE DIVERSITY OF GERMAN TOP ORGANISATIONS’ WEBSITES AND SOCIAL MEDIA
Bibliographic record
Abstract
Considering the potential impact diverse and not diverse communication could have on an organisation’s stakeholders there is a lack of research about how diversity is communicated externally by organisations. Different studies have shown that gender-neutral language can positively influence peoples’ attitudes towards members of the LGBTQIA+ community and that pictures can have a significant influence on their recipients. Therefore, the goal of this article is to find out how diverse the communication of organisations in Germany is. Through a quantitative analysis of media content, the websites and Instagram accounts of twelve German companies chosen through a ranking of Germany’s best employers are analysed. The study’s results show that the external communication of the analysed companies is not yet completely diverse, that there are differences between the diversity communication on the companies’ websites and on social media and that the company size doesn’t seem to be connected to the level of diversity. Furthermore, some companies are starting to adapt gender-neutral language and also show symbols that show support of the LGBTQIA+ community. Ultimately, the study shows that companies still need to improve on some levels, especially regarding the representation of people with disabilities and members of the LGBTQIA+ community, until their communication can really be seen as diverse - and until they will potentially have an impact on their stakeholders.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".