(De)regulating access to tourism and hospitality professions: The case of Portugal
Bibliographic record
Abstract
This study explores the perceptions of key stakeholders responsible for tourism and hospitality (T&H) organisations in Portugal regarding deregulating access to professions in T&H labour. A qualitative research approach was utilised. Through in-depth interviews, data was collected purposefully from the participants. According to the qualitative findings, opinions on regulating and deregulating professions in T&H labour emerged as against deregulation, against regulation, and moderate. However, most opinions fall under the against deregulation category. Moreover, deregulating access to the profession has both positive and negative impacts, and the influences of deregulation on working models include two sub-themes: self-employment and accumulation of functions. Finally, policymakers' responses to deregulation include elements such as the increase in the number of associates that joined associations, the partnership of associations with educational institutions to create certifications, and the creation of unions for specific positions. This research contributes valuable insights from key stakeholders on the deregulation of professions in T&H labour in Portugal, providing policymakers and scholars with a better understanding of the viewpoints on regulating and deregulating professions in this sector.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".