Politicians' temporal focus

Lead Research Organisation: Royal Holloway, Univ of London
Department Name: Politics, Internatl Relations & Philos

Abstract

Some of us dwell on the past. Others live in the present. Others still look towards the future. The degree to which our thoughts are directed to the past, present or future is called our *temporal focus*. Psychologists have studied people's temporal focus, and have found that future-focused individuals are more likely to engage in pro-social behaviours and perform well in their studies and in their careers.

This project is about politicians' temporal focus. Politicians are often accused of having a particular temporal focus-of focusing too much on the present, or of being "short-termist". This focus (runs the argument) prevents politicians from tackling long-term challenges such as climate change or caring for different generations. Tackling these challenges can involve making sacrifices now in order to gain advantages later. Politicians (and the voters who elect them) may discount these future benefits.

The problem is that we don't know whether politicians are short-termist in this way. Indirect evidence is just that-indirect. Politicians who neglect climate change might do so because of short-termism, but might also do so because they don't believe in climate change, or believe that costs of tackling climate change outweigh the benefits. Direct evidence is better, but harder to collect. It is difficult to convince MPs to answer survey questions about their attitudes, and impossible to do so for historical politicians.

This project solves this problem by developing an unobtrusive measure of politicians' temporal focus by looking at the language they use. Computational linguists have shown how to extract different features-parts of speech, dates, and abstract references to the future or past-from large bodies of text in an automated fashion. Psychologists have shown how these features of a person's language use can be used to predict their temporal focus. These studies have been carried out on short texts (typically social media posts) by young adults or students.

We extend these techniques to cover politicians' speech, and produce measures of politicians' temporal focus for politicians in 3 national parliaments (the UK Parliament, the Australian Senate, and the Finnish Eduskunta). We test whether these measures make sense by comparing them to questionnaire responses from a small group of politicians in the UK Parliament, surveyed in collaboration with the All-Party Parliamentary Group on Future Generations. We then go on to show how politicians' temporal focus varies according to age and different political and life events, and compare temporal focus in politicians to temporal focus in the general population.

Knowing about politicians' temporal focus is valuable for its own sake, but it is also valuable because it allows us to answer questions about how we design our political institutions. Our project looks at three different institutional choices: the choice to elect or appoint politicians, the choice to have longer or shorter parliamentary terms, and the choice to have specialised institutions which focus on the future. By careful within-country
comparisons, we test whether particular institutional choices change politicians' temporal focus beyond what we would expect as a result of ageing and chance events.

Our project has concrete benefits for countries considering institutional reforms. In the UK, numerous groups have called for "more long term thinking in UK policy". In New Zealand, party leaders have expressed willingness to lengthen parliamentary terms to avoid short-termism. If we want to avoid short-termism, and promote a different temporal focus in our politicians, we need to be able to measure temporal focus, and relate temporal focus to different institutional choices. This research will do just that.

Publications

10 25 50
 
Title Fine-tuned large language model for predicting the temporal focus of parliamentary speech 
Description This model is a fine-tuned version of distilber-base-cased designed to classify parliamentary speech into past, present or future orientation. The training data for the model consists of roughly 3,600 sentences from the UK House of Commons Hansard which have been hand-coded by two coders working independently and reconciling their differences. Sentences have been pre-processed using Duckling to turn absolute temporal references into relative temporal references. Thus, the sentence "In the year 2050, global temperatures are forecast to rise by 1 degree. " said in the year 2023 becomes "27 years from now, global temperatures are forecast to rise by 1 degree. " Note that sentences which dealt with conditional claims or were in the irrealis were classified as present-oriented. Sentences or sentence fragments which could not be classified in any other way were classified as present-oriented. The distribution of temporal foci in the training data was as follows: Present: 2421 instances Past: 825 instances Future: 363 instances 
Type Of Material Computer model/algorithm 
Year Produced 2024 
Provided To Others? Yes  
Impact None yet. 
URL https://huggingface.co/chanret/tfs_distilbert
 
Title Fine-tuned large language model for predicting the topic of parliamentary speech 
Description This is a fine-tuned version of the distillBERT case-sensitive English language model. It predicts sentence-level topics using data supplied by the Comparative Agendas Project. 
Type Of Material Computer model/algorithm 
Year Produced 2024 
Provided To Others? Yes  
Impact None yet. 
URL https://huggingface.co/chanret/hoc_cap_distilbert
 
Description Student group visit 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Undergraduate students
Results and Impact Gave a talk to the University of Aberdeen student politics society on age and politicians' temporal focus.
Year(s) Of Engagement Activity 2024