’Soft’ tourism planning in Denmark:Towards a more settled format?
Bibliographic record
Abstract
The need for ‘soft’ (non-mandatory) tourism planning has long been recognised in the tourism planning literature (Gunn & Var, 2002). Although planning for tourism can be seen as a form of sectoral planning (Halkier, 2010), that fact that this socio-economic activity has a strong spatial dimension in that it takes place in particular locales, tourist destinations, that may or may not correspond to existing borders between e.g. local authorities. ‘Soft’ tourism planning and land-use planning are in other words likely from the outset to exist in a potentially awkward relationship. In Denmark the 2007 local government reform gave rise to significant increase in non-statutory planning (Hansen, 2017), bringing with it a generally ‘softer’ planning culture in Danish municipalities. This paper argues, however, that the basic tensions around ‘soft’ tourism planning remain ongoing challenges that public and private stakeholders need to address, in particular 1) inter-municipal relationship when planning for tourist destinations cut across existing political borders, and 2) relations between public, private and civic stakeholders in ‘soft’ planning processes. The paper proceeds in four steps. First a review of the existing literature on ‘soft’ tourism planning, focusing especially on its relationship to other forms of spatial planning. Then a comparative analysis is undertaken between the pre-2007 and post-2007 periods (Henriksen & Halkier, 2009; James & Halkier, 2019), focusing on prominent examples of ‘soft’ tourism planning in Denmark (Regional Tourist Boards and inter-municipal tourism alliances in the early period, inter-regional alliances and Destination Management Organisations in the later period). The paper concludes with a discussion of the evolving challenges of ‘soft’ tourism planning, arguing that the challenges of horizontal coordination between municipalities and engagement of relevant stakeholders may have taken new forms but remains difficult territory to navigate for all parties involved. The paper is part of UP:DK project sponsored by Realdania, Dansk Kyst & Natur Turisme, and Aalborg University.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".