Scientists Retrace 30k-Year-Old Sea Voyage, in a Hollowed-Out Log
nytimes.com
nytimes.com
So what if those people back then had other beliefs that made them act accordingly „unnatural“ to us nowadays? Till this can be excluded with a certainty, this study is merely a shallow response and let’s people believe uncertainties instead of knowing historical facts.
> Dr. Kaifu noted that the islands could be spied from the top of one of Taiwan’s coastal mountains, indicating intentional travel
Image?
http://english.ryukyushimpo.jp/wp-content/uploads/2011/09/Yo...
Most triathlons are probably won by men and global exploratory voyages were first accomplished by men...
Not saying your wrong I'm asking what I'm missing?
This assumption seems strange. How do you think polynesians spread without both men and women?
If you're referring to the european age of exploration, they weren't rowing much at that point, rendering sex mostly irrelevant.
Off-hand this seems like it'd require twice the time, resources, and risk. Without some strong theory as to why an all-male boat would be particularly useful (but not necessary on the return trip), occam's razor suggests to me that this is a much less likely theory.
FWIW, the oral history does indicate both men and women were part of at least the initial settling of new zealand by polynesians: https://en.wikipedia.org/wiki/Kupe. Even if this is entirely mythical, it does speak to the cultural understanding of settling.
we of course wouldn't have exidence if that happened as cultures change. Whatever caused a desire to settle would change culture.
> The boat was paddled by a mixed team consisting of four men (paddlers) and one woman (steerer), without replacement by other paddlers on the way. The inclusion of both men and women is essential because our focus is ancient migration, not men’s adventure. An unstable, round-bottomed dugout required skilled paddlers to control it on the open sea. Furthermore, because this type of boat does not travel in a straight direction but instead snakes its way, the skill of the steerer is crucial for optimizing its performance. Considering these factors, we invited professional and semi-professional sea kayakers as the paddlers and steerer.
And ... this wasn't a race? Perhaps 5 women could have gotten there faster, but speed wasn't the primary goal.
I don't know anything about paddle sports specifically, but I have seen a discussion about sex differences in performance in other endurance sports, and one important distinction is that women as a group can have average pace times that are faster than men as a group, even when most of the outright winners are men.
https://www.theguardian.com/lifeandstyle/2024/dec/30/the-lon...
> While men still hold the edge, women’s rapid progress suggests a future where they may outperform men in extreme endurance events.
https://www.bbc.co.uk/sounds/play/p0hg2764 (9 minute episode).
Why is that important? Unless the runners are conscripted into the race, it's not telling you anything about women or men.
Your second link notes this explicitly:
>> What the data actually means is that after 195 miles the average pace of all women competing is better than the average pace of all the men competing. Why is this the case? Math and demographics (not physiology and toughness). In any athletic user group, the early adopters are also higher performers. Take the people who pioneered skateboarding, or adventure racing as an example. Those early adopters were good at the sport they were trying to push the boundaries. As a sport becomes more and more popular, the number of non-elites grows much faster than the number of elites. Therefore, even though the best times and performances improve (by way of a course or world record) the average times get worse. Ultrarunning is no different.
It's not saying something about _all_ men or women. If you did try to work with a random sample wouldn't you mainly find that almost all people of both sexes can't run an ultra marathon?
But in making comparative statements even about people who choose to participate in such races, I think a critical distinction made in that article is that there's a difference between "E(Pace_W) < E(Pace_M)" vs "Min(TotalTime_W) <? Min(TotalTime_M)".
The earlier anecdote was making a statement about who won a canoe race and using it as evidence of a group level difference ... But race winners are the extreme end of the distribution and are poor information about the overall behavior.
Compare http://www.lagriffedulion.f2s.com/dogrun.htm :
>> How, for example, do we determine a distribution of running ability within an entire population? Can we find a representative sample of tribesmen, provide each with motivation and training, and finally measure their times for some event? Not very likely. There is, however, a way out. In Aggressiveness, Criminality and Sex Drive by Race, Gender and Ethnicity, we introduced the method of thresholds. It applies nicely to this problem. The proportion of each tribe meeting or exceeding some threshold of performance is the only input it requires. When all is said and done, the precise definition of "ability" will still be fuzzy, a characteristic of the method of thresholds. That aside, we will have established running ability distributions in tribes relative to one another.
>> Some of the data we need are available from chroniclers of track and field. All-time-best lists are particularly useful. For a given event, such a list might contain 100, 500, 1500 or any number of the best times ever run. The slowest time on a list serves as the threshold of performance required by the method of thresholds.
> If you did try to work with a random sample wouldn't you mainly find that almost all people of both sexes can't run an ultra marathon?
No, you'd find that people managed to go different distances before failing. You would have to be intentionally avoiding the result you expected to find to binarize your outcome data like that. The data you're appealing to right now isn't binarized.
> But in making comparative statements even about people who choose to participate in such races, I think a critical distinction made in that article is that there's a difference between "E(Pace_W) < E(Pace_M)" vs "Min(TotalTime_W) <? Min(TotalTime_M)".
The article itself provides the explanation: there are very, very few women running. What lesson do you feel we should draw? To me it looks like the lesson is "men are a lot more interested in distance running than women are".
> Suppose PA(x) and PB(x) differ only by a translation in x, such that fB(x) = fA(x - Δ), where Δ is the mean difference in x between the groups.
... but you can't really just assume that the variance within two groups is the same. Especially when comparing a small group like the Nandis (a "subtribe of a half million") vs a large and diverse group like "Europeans", as they do in the "Augmentation of Small Differences" section, it seems pretty cavalier to just assume that the variance is the same.
But for comparing stuff between sexes, the "variability hypothesis" about men having greater variability across a range of traits dates back to Darwin, and has a pile of research results. It would seem especially irresponsible to rely on an assumption that the variance was equal between the sexes. One might have prior reason to expect otherwise, and differences in variance may materially contribute to different fractions above or below an extreme threshold.
Further, given the kind of selection effects you were alluding to before, it may not even be safe to assume these distributions are normal. While the endurance of the broader population ought to be normally distributed, if there are various hurdles on the path to participating (e.g. the organizers say you should probably have completed a 50 mile race at a given pace before signing up for their 100 mile race?) one might well see a different overall shape.
That would imply a much larger advantage for men. If you were seeking to show that women outperform men, you'd gloss over that point as much as you thought you could get away with.
(In a little more detail: if you determine that it takes an athleticism factor of 10.8 to run 200 miles, greater male variability immediately implies that among all people who have that much athleticism, the average male athleticism will be quite a bit higher than the average female athleticism. The average of a thresholded normal distribution is quite close to the threshold, but it gets farther away as the standard deviation increases.)
> Further, given the kind of selection effects you were alluding to before, it may not even be safe to assume these distributions are normal. While the endurance of the broader population ought to be normally distributed, if there are various hurdles on the path to participating (e.g. the organizers say you should probably have completed a 50 mile race at a given pace before signing up for their 100 mile race?) one might well see a different overall shape.
...you don't seem to have understood the method. The assumption is that the broader population is normally distributed - you know, what you already said it was safe to assume - and that the selected population consists of that part of the broader population's normal distribution that exceeds some threshold.
Or in pictures, we assume that the population looks like this:
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