International Passenger Survey, 2015
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.<br> <br> 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. <br> <br> <b>Weighting the IPS</b><br> 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.<br> <br> <b>Data contents</b><br> For the fourth edition (June 2016) the data and documentation were updated to cover the whole year for 2015.<br> <br> The <i>International Passenger Survey</i> (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.<br>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.045 | 0.017 |
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; both teacher heads agree on what is shown here.
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".