Cartes conceptuelles des facteurs critiques à la transition vers des espaces ouverts et flexibles sur la performance et le bien-être
Bibliographic record
Abstract
Many organizations are considering reconfiguring their workspaces to optimize them. While studies (Brunia et al., 2016; Dunford et al., 2013; Hoendenvanger et al., 2016) on open and flexible spaces tend to show mixed results, it is important to consider experimenting and transitioning to these new spaces so that they best meet the needs of the change recipients and the organization. A pilot project was built for recipients to experiment with new spaces. A support team followed 15 cohorts of about 15 people for two to three months each, over three years. Exploratory action research in a university setting made it possible to collect qualitative data from weekly follow-up meetings, a survey, and videotaped statements. Concept maps created (Davies, 2011) from 260 verbatim excerpts illustrate the hierarchical relationships between critical factors and their psychosocial effects. The results show three critical factors. The diversity and flexibility of the layouts facilitated adaptation and created a sense of increased performance by meeting the needs for focus, privacy, and teamwork. Physical proximity contributed to increased social interaction and accessibility to colleagues and managers, leading to collaboration and a sense of well-being. Change management fostered the perception of organizational support and spatial appropriation. Change management practitioners and leaders might consider a transition period of a few months for learning and monitoring of recipient groups before final decisions are made. The results obtained need to be contextualized and could vary by type of position. The effects of these spaces in the long term and consideration of tasks performed during the days spent in the office would be interesting leads for future studies.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".