Different Stories for Different People - Engagement with the Archaeology of HS2 Area North
Bibliographic record
Abstract
The UK high-speed railway High Speed Two (HS2) will link London and the Midlands following the route of the 19th-century London and Birmingham Railway. After years of work, including the largest programme of historic environment investigation in the UK across a swathe of the landscape over a number of years, the construction stage is now in progress. The lead document for the delivery of the historic environment works is HS2's generic Written Scheme of Investigation, the Historic Environment Research and Delivery Strategy (HERDS). One of the central principles of HERDS is to derive public benefit from the historic environment works, by meeting community engagement objectives and building a legacy of knowledge and skills. This article was delivered as a paper at the European Association of Archaeologists' (EAA) conference in 2023, themed 'Weaving narratives', in an HS2 session entitled 'Different stories for different people'. It discusses some of the principles of audience and narrative development that can be transferred to other archaeological projects from the discoveries in the Midlands (HS2 Area North). Three steps are highlighted. Firstly, engage with and listen to stakeholders and community representatives early in the project lifespan, using professional expertise. Secondly, assimilate key themes and local issues, create partnerships, and identify heritage champions to support the design of activities. Thirdly, work together to deliver a range of events and activities tailored to a variety of audiences using bespoke platforms and styles. Adopting this approach, and having clearer mechanisms for measuring and evaluating the benefit of the outcomes, demonstrates worth and benefits the sector. For project legacy, the goal is to use the stories to transfer skills, information, good practice and ownership.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".