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Reposted by Paul Bose

Paul Schaefer @pauschae.bsky.social · Aug 27

Looks amazing!

Grant McDermott @gmcd.bsky.social · Aug 25

To borrow another example, taken from the `dbreg` README: github.com/grantmcdermo...

Here I am running a fixed-effects regression on 180 million(!) row parquet dataset... and it completes **< 2 seconds**... on my laptop 🤯

This is powered by @duckdb.org under the hood.

#rstats #econsky

Running dbreg::dbreg() on a full year of NYC taxi data... and it takes less than 2 seconds.

dbreg(
tip_amount ~ fare_amount + passenger_count | month + vendor_name,
path = "read_parquet('nyc-taxi/**/*.parquet')", ## path to hive-partitioned dataset
vcov = "hc1"
)
#> [dbreg] Estimating compression ratio...
#> [dbreg] Data has 178,544,324 rows and 24 unique FE groups.
#> [dbreg] Using strategy: compress
#> [dbreg] Executing compress strategy SQL
#>
#> Compressed OLS estimation, Dep. Var.: tip_amount
#> Observations.: 178,544,324 (original) | 70,782 (compressed)
#> Standard Errors: Heteroskedasticity-robust
#> Estimate Std. Error t value Pr(>|t|)
#> fare_amount 0.106744 0.000068 1564.742 < 2.2e-16 ***
#> passenger_count -0.029086 0.000106 -273.866 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

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Paul Bose @pbose.bsky.social · Jul 27

At the time I was using both inline completions and the chat function (at least the inline chat, not the agent capabilities, which didn't exist yet).

Paul Bose @pbose.bsky.social · Jul 2

Interesting, thanks for sharing. I didn't know about this. Tbh I mostly did this exercise to test out the gender and region prediction package. But thought the results might be interesting. Nice to know that there is a resource directly from RePEc.

Paul Bose @pbose.bsky.social · Jul 2

Check out my blog post for a detailed explanation of my methodology and findings: www.paulbose.com/thisandthat/...

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Paul Bose @pbose.bsky.social · Jul 2

I used the "nametrace" package (github.com/parobo/namet...) to predict gender and region of origin based on author names rather than checking each author's actual gender or background. Therefore, take the results with a grain of salt.

GitHub - parobo/nametrace: A python package to predict demographic information from names.

A python package to predict demographic information from names. - parobo/nametrace

github.com

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Paul Bose @pbose.bsky.social · Jul 2

The rise in female representation in the top 5% appears to be a global phenomenon, with increases observed in both North America/Europe and the "rest of the world." The trend is slightly stronger in North America and Europe, but these regions also had more ground to cover in terms of catching up.

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Paul Bose @pbose.bsky.social · Jul 2

The growth for regions other than US/Canada/Europe is primarily driven by scholars from Eastern Asia, Southern Asia and South America. Unfortunately, Africa remains extremely underrepresented.

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Paul Bose @pbose.bsky.social · Jul 2

While these numbers are super low, there's a small of positive change, ... a slow one.
- The share of women has edged up from roughly 9% to 12% over the past 12 years. Progress, but the pace is too slow!
- Representation from "the rest of the world" has increased from 16% to 25% in 2025.

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Paul Bose @pbose.bsky.social · Jul 2

How is economics doing in terms of representation of women and researchers with a region of origin outside North America or Europe?
I looked at the top 5% authors on RePEc in the last 12 years.
- Only 12% are women
- 25% a region of origin other than US/Canada/Europe

#EconSky #AcademicSky #PoliSky

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Paul Bose @pbose.bsky.social · Jun 26

#econsky #polisky

Paul Bose @pbose.bsky.social · Jun 26

Need to predict people's gender or region of origin from their name for your research? Check out my python package "nametrace" which provides a simple modern API to do just that.

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Paul Bose @pbose.bsky.social · Jun 25

It was great to be in Clermont Ferrand to present my work with @econom.bsky.social on local social media activity after refugee arrival. Thanks so much to the organizers and the other participants. It was a super interesting workshop!

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Reposted by Paul Bose

Zohal Hessami @zohalhessami.bsky.social · Jun 5

🚨3 days left to apply!

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Paul Bose @pbose.bsky.social · May 27

Our results suggest that refugee influx caused a sharp but short lived spike in salience of refugees. People who remained active tweeters on the topic started to show more opposition of refugees after a while however.
We combine the analysis with extremely local voting data and find similar results.

Paul Bose @pbose.bsky.social · May 27

Olivier Marie is presenting our paper (joint with Renske Stans) on social media salience of refugees and election effects in Linnaeus today. I am super excited that this paper ready to be presented more widely!

We study local twitter discourse around the timing of refugee arrival during 2015/2016.

Department of Economics and Statistics, Linnaeus University @linnaeus-econ.bsky.social · May 26

Tomorrow we'll have @econom.bsky.social from the Erasmus School of Economics in Rotterdam visiting us! 🤩 Olivier will present his ongoing work "From Tweets to Ballots: Refugee Inflows and Natives' Reactions". Welcome!

Olivier MARIE

Welcome to my website! I am Professor of Labour Economics at the Erasmus School of Economics in Rotterdam. I have research affiliations at: TI, IZA, CEPR, and CESifo. My full CV is here. My main...

sites.google.com

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Paul Bose @pbose.bsky.social · May 20

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Paul Bose @pbose.bsky.social · May 20

As an example I show how to analyze the sentiment of the last 100 posts of the reddit CEOs spez and kn0thing in mere seconds.

Paul Bose @pbose.bsky.social · May 20

Are you using LLMs for your research and want to classify millions of text? This can be a very slow and expensive process. But it doesn't have to be. In my blogpost I explain how to use multiple GPUs and vLLM to analyse thousands of texts with an LLM super fast!

#econsky #polisky

lnkd.in/di9AbciU

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Paul Bose @pbose.bsky.social · May 7

Want to learn how to finetune a large language model for your specific needs? I wrote a short post on how to train LLAMA for a classification task:
www.paulbose.com/thisandthat/...

#econsky #polisky

Finetuning an LLM to do classification | Paul Bose

LLMs are ubiquitous at this point. ChatGPT for example is constantly used to generate labels for unstructured data such as text. However using ChatGPT might be too expensive or not perform very well f...

www.paulbose.com

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Paul Bose @pbose.bsky.social · Feb 26

BONUS: use ollama to interact with the models from python and receive structured responses. This is super helpful for classification or structured outputs for e.g. Text summary.

Paul Bose @pbose.bsky.social · Feb 26

Want to learn how to run your own version of Deepseek R1 or Meta's LLAMA model on a remote high-performance computing server? I wrote a brief blog post explaining how you can install and run the models using ollama.

www.paulbose.com/thisandthat/...

#econsky #polisky

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Paul Bose @pbose.bsky.social · Feb 6

FYi, in case you might care, here is an explanation how the models moderate internally (i.e. without using the API, but using system prompts).
www.lesswrong.com/posts/jGuXSZ...

Refusal in LLMs is mediated by a single direction — LessWrong

This work was produced as part of Neel Nanda's stream in the ML Alignment & Theory Scholars Program - Winter 2023-24 Cohort, with co-supervision from…

www.lesswrong.com

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Reposted by Paul Bose

Francesco Sobbrio @fsobbrio.bsky.social · Jan 29

Last chance to submit a paper (Deadline Jan 31). 3rd CEPR Workshop on Media, Technology, Politics, and Society cepr.org/events/3rd-b...

3rd BOCCONI - CEPR Workshop on Media, Technology, Politics, and Society

Search the site

cepr.org

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Paul Bose @pbose.bsky.social · Jan 28

I seems the abliterated versions are not censored but the the base model seems to be:
bsky.app/profile/paul...

Paul Goldsmith-Pinkham @paulgp.com · Jan 28

Abliterated model has no censorship issues on this one at least!!

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Paul Bose @pbose.bsky.social · Jan 28

Interesting point, your screenshots do somewhat indicate hard coded instructions in the model, but it could still be only on the API version. Would like to see this for the local model.

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