International Passenger Survey, 2012
Bibliographic record
Abstract
The data cover four subject areas, termed 'Airmiles', 'Alcohol', 'Qregtown' and 'Qcontact'. One file is produced each quarter per subject area, and the dataset updated quarterly. These files can be joined together using the variables YEAR, SERIAL, FLOW and QUARTER. The depositor recommends that only expert users who are very familiar with the coding and weighting structures use these data, as limited support is available. Some considerable understanding of the data is required before meaningful analyses can be made; care must be taken when performing time series operations as codes can vary from year to year and not all variables from one year's dataset are used in other years. Weighting the IPS ONS advise that the variable 'fweight' included in the 'Qcont' dataset should be applied to get an overall weighted profile. This weight is set consistently over time. Other weights are provided to analyse finer detail but no information is provided in the documentation. ONS is currently reviewing the documentation but in the meantime if users have any detailed weighting questions they should contact ONS at: socialsurveys@ons.gov.uk. Data Contents The first edition deposited in September 2012 included Quarter 1 data and documentation. The second edition deposited in November 2012 included the Travelpac version of the IPS data for Quarters 1 and 2 and updated documentation. The third edition deposited in February 2013 included data and documentation from Quarter 3, 2012. The fourth edition deposited in May 2013 included data and documentation from Quarter 3, 2012. The fifth edition deposited in June 2013 included data and documentation from Quarter 2, 2012. The International Passenger Survey (IPS) aims to collect data on both credits and debits for the travel account of the Balance of Payments, provide detailed visit information on overseas visitors to the United Kingdom (UK) for tourism policy, and collect data on international migration.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.073 |
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".