Mattering in the metaverse: Re-imagining inclusive futures of work with immersive platforms
Bibliographic record
Abstract
Immersive platforms have become increasingly embedded in the workplace, leading to questions about their effects on the future of work. For immersive platforms to be successful, they need to be inclusive. The future of work with immersive platforms involves designing for inclusive purposes. This commentary proposes mattering, i.e., the experience of feeling seen, valued and significant to others, as a foundational concept to understand the future of work with immersive platforms. This commentary argues that mattering provides a rich lens to engage critically with immersive platforms and work and to make sense of their strategic implications. While immersive platforms promise co-presence, embodied interactions, and access, they may reproduce precarity, marginalization, and algorithmic forms of exclusion. This commentary questions how immersive platforms challenge and enable mattering at work, discussing how they can affect workers’ recognition, contributions, and sense of dignity in diverse virtual or hybrid organizational settings. The commentary calls for a design and research agenda on immersive platforms that are not only evaluated by pre-existing performance measures but also by their capacity to cultivate mattering. This agenda is important to strategic IS scholars and practitioners committed to shaping inclusive futures of work.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.019 | 0.032 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| 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".